Published August 14, 2026 · Fact-checked against the August 12 launch announcement and current API documentation
Grok 4.6 Review: Frontier AI Performance at a Price That Changes the Conversation
Grok 4.6 arrived on August 12 with a simple pitch: frontier-level intelligence, stronger long-running agent performance, and a much lower headline price than the most capable models from OpenAI and Anthropic. The numbers are strong enough to deserve attention. They are not strong enough to justify every “number one” claim now circulating online.
The most useful way to read this release is not as another AI leaderboard victory. It is as a buying decision. Can Grok 4.6 complete coding, research, analysis, and app-building work reliably enough to replace a more expensive model? Does its $2-per-million-token input price survive contact with real, long-context workloads? And do the gains over Grok 4.5 show up where developers actually feel them?
After comparing SpaceXAI’s launch materials, current API documentation, independent Artificial Analysis measurements, coverage published on August 12 and 13, and more than 30 comments across relevant Reddit discussions, the answer is nuanced. Grok 4.6 is one of the most compelling price-to-performance releases of 2026. It is also slower to begin responding than several rivals, can be verbose, becomes twice as expensive when a prompt crosses 200,000 tokens, and still carries a trust problem that a benchmark chart cannot erase.
This guide separates what SpaceXAI claims, what independent testing supports, and what remains unproven.
What Is New in Grok 4.6?
SpaceXAI describes Grok 4.6 as an upgrade focused on “long-running agents” and more ambitious interactive and visual work. In plain English, the model is supposed to remain useful after the first answer. It should research unfamiliar material, navigate a large codebase, use tools across many steps, build a substantial first version of an application, test its work, and continue improving it through feedback.
That emphasis matters. The AI market is moving away from one-shot prompts and toward systems that stay active for minutes or hours. A chatbot can explain how to fix a bug. An agent has to inspect the repository, locate the fault, edit several files, run tests, read the failures, correct the implementation, and report what changed. The longer that chain becomes, the more opportunities a model has to lose the goal, repeat work, misuse a tool, or confidently declare success too early.
According to the official Grok 4.6 announcement, the new model received a longer supplemental training run than Grok 4.5. SpaceXAI says it used curated model-generated reasoning data, technical and engineering data, a revised optimizer, supervised fine-tuning trajectories generated with Grok 4.5, and reinforcement learning across general coding, knowledge work, kernel optimization, web development, and computer-aided design.
Those details tell us where SpaceXAI spent its effort. They do not independently prove the result. The evidence comes from the evaluation scores, third-party measurements, and eventually production use.
Availability at launch
Grok 4.6 launched in Cursor and Grok Build, with API access through SpaceXAI and partner availability through services including OpenRouter, Vercel, and Cloudflare. SpaceXAI offered double the included Grok 4.6 usage in Cursor and Grok Build for the first week. That launch-week promotion is temporary and should not be treated as the model’s normal cost.
The API model name is grok-4.6. The current documentation lists a 500,000-token context window, configurable reasoning, text and image input, and text output. Its built-in knowledge cutoff is February 1, 2026. It does not know later events unless a developer enables a current-data tool such as web or X search.
- July 20, 2026: Grok 4.5 launches with an emphasis on coding, agents, and knowledge work.
- August 12, 2026: SpaceXAI introduces Grok 4.6 and makes it available through Cursor, Grok Build, the API, and partners.
- August 12, 2026: Artificial Analysis publishes independent results placing the model back on the intelligence-versus-cost frontier.
- August 12–13, 2026: Technology and financial coverage focuses on the model’s GPT-5.6-level composite score and lower token price.
Why Grok 4.6 Matters Right Now
For buyers, the immediate problem is model sprawl. A development team can now choose among several frontier models, cheaper mid-tier models, coding-specific agents, open-weight systems, and multiple reasoning levels. Benchmark scores cluster closely at the top, while the cost of a completed task can vary dramatically.
Grok 4.6 matters because it pressures the most expensive part of that market. Its independent composite score sits near the leaders, but its short-context output rate is $6 per million tokens. Output is often the expensive side of agentic work because reasoning models may generate substantial hidden reasoning and visible completion tokens while iterating.
Artificial Analysis measured an average cost of $0.84 per Intelligence Index task. That number reflects the model’s token use during a defined benchmark suite; it is not a universal quote for your job. Still, it gives the headline pricing a reality check. Grok 4.6 did not earn its efficiency label solely because SpaceXAI published a low rate card. Independent testing found that it completed the suite at a competitive total task cost.
There is also a strategic story. 9to5Mac framed the release as part of SpaceXAI’s attempt to rebuild Grok’s standing, pointing to its closer integration with Cursor and the arrival of Grok Bot. Financial coverage on August 13 treated the model as another step in a widening competition among SpaceXAI, Anthropic, and OpenAI. The important market signal is not that one launch permanently defeated the others. It is that frontier capability is becoming cheaper, and high-end vendors have less room to charge a premium without proving a workload-specific advantage.
Grok 4.6 Benchmarks: Strong Results, Different Winners
The safest summary is this: Grok 4.6 is competitive with the frontier across a broad set of agentic, coding, and knowledge-work evaluations. It does not win every test.
On SpaceXAI’s published table, Grok 4.6 High scores 61 on the Artificial Analysis Intelligence Index. That matches GPT-5.6 Sol Max in the same table and trails Claude Fable 5 Max by one point. Artificial Analysis independently reports the same score of 61, placing Grok 4.6 behind Claude Opus 5 at 63 and Claude Fable 5 at 62, while slightly ahead of Kimi K3 at 60.
The index combines nine evaluations rather than relying on a single coding test. That breadth makes it more informative than one leaderboard, but it remains a benchmark. It cannot perfectly represent your codebase, your tools, your review standards, your data, or the cost of a failed deployment.
ALT: Grok 4.6 benchmark comparison with GPT-5.6 Sol, Claude Fable 5 and Grok 4.5 across intelligence, coding and agent tasks
| Evaluation | Grok 4.6 High | Grok 4.5 High | GPT-5.6 Sol Max | Claude Fable 5 Max |
|---|---|---|---|---|
| AA Intelligence Index | 61 | 56 | 61 | 62 |
| GDPVal-AA v2 | 1753 | 1526 | 1728 | 1741 |
| CursorBench v3.2 | 69.9% | 66.7% | 67.2% | 70.5% |
| DeepSWE v1.1 | 65.9% | 54.0% | 73.0% | 70.0% |
| FrontierCode v1.1 Extended | 61.3% | 56.6% | 60.6% | 63.6% |
| APEX-Agents | 57.5% | 47.1% | 56.7% | 59.2% |
| Terminal-Bench v3.0 | 26.0% | 15.7% | 34.6% | 34.1% |
| AA-Briefcase | 1577 | 1313 | 1502 | 1574 |
Source: SpaceXAI launch table. Third-party competitor scores were drawn by SpaceXAI from published system cards or public leaderboards. Benchmark versions matter; do not compare a Terminal-Bench v3.0 score directly with a v2.1 score.
The clearest improvement is over Grok 4.5
The comparison with Grok 4.5 is more persuasive than the comparison with competitors because the scores were presented under the same release table and evaluation versions. Grok 4.6 improves across every listed test. The five-point gain on the Artificial Analysis index is substantial for an incremental model number, while CursorBench rises from 66.7% to 69.9%, DeepSWE from 54.0% to 65.9%, and APEX-Agents from 47.1% to 57.5%.
That pattern supports SpaceXAI’s claim that this release is about sustained engineering and agent work rather than a cosmetic refresh. It does not mean every Grok 4.5 user will see a dramatic change in simple chat. The gains are most likely to appear in multi-file changes, tool use, research, long sequences, and tasks that require repeated correction.
Independent measurements reveal the tradeoffs
Artificial Analysis reports output speed around 65.5 tokens per second and a time to first token of 31.18 seconds for Grok 4.6 High through the first-party API. Its comparison group medians were about 68.7 tokens per second and 2.89 seconds to first token. In other words, the model’s sustained generation speed is close to the group average, but the initial wait can be much longer.
The same evaluator classifies Grok 4.6 as somewhat verbose: it generated 72 million output tokens across the Intelligence Index, compared with a 70-million median. That difference is not enormous, but it reinforces a point raised repeatedly by developers: a cheap per-token model can still produce a larger bill if it uses more tokens or takes unnecessary turns.
Long-horizon results are encouraging. On Artificial Analysis’s private AA-Briefcase evaluation, Grok 4.6 reached an Elo of 1577. The evaluator says the model averaged about 53 turns and 0.5 billion input tokens across the run, versus roughly 103 turns and 2.0 billion input tokens for Claude Opus 5 Max. This does not make Grok universally better than Opus. It suggests that on this specific suite, Grok reached a comparable class of result with far less accumulated context.
Grok 4.6 API Pricing: The Headline and the Catch
The widely repeated price is accurate for short-context requests:
- Input: $2 per million tokens
- Cached input: $0.50 per million tokens
- Output: $6 per million tokens
But the official SpaceXAI pricing page contains an important condition. Once the prompt reaches 200,000 tokens, the long-context rate applies to all tokens in that request, not only the portion above 200,000.
- Long-context input: $4 per million tokens
- Long-context cached input: $1 per million tokens
- Long-context output: $12 per million tokens
This threshold matters for the exact workloads Grok 4.6 is marketed to handle. A large repository, a long research session, several attached documents, or an agent with an expanding conversation history can cross 200,000 tokens. The context window remains useful, but “500K context at $2/$6” is not a complete cost description.
Priority processing is another multiplier. SpaceXAI charges twice the standard rate when a request is served at the priority tier. Server-side tools are billed separately: web search, X search, and code execution are currently listed at $5 per 1,000 calls, while attachment search is $10 per 1,000 calls and collections search is $2.50 per 1,000 calls. Agent costs therefore depend on token volume, cache reuse, tool-call frequency, and the number of repair loops.
A realistic cost example
Suppose a coding agent processes 150,000 fresh input tokens and generates 20,000 output tokens. At short-context rates, the token cost is approximately:
(0.15 × $2) + (0.02 × $6) = $0.42
If the same session expands to a 220,000-token prompt and generates 20,000 output tokens, the full request moves to long-context pricing:
(0.22 × $4) + (0.02 × $12) = $1.12
The second job contains only 70,000 more input tokens, but the estimated bill is more than double because every token in the request is repriced. Caching can reduce that cost, but only when the provider actually records a cache hit. Teams should log fresh input, cached input, output, reasoning, and tool calls rather than multiplying the headline rate by a rough token estimate.
If you want to model different traffic levels, use MusTrend’s AI API Cost Calculator (2026) – GPT, Claude & Gemini Pricing as a starting framework, then enter Grok’s current rates and add a separate scenario for requests at or above 200K tokens.
Grok 4.6 vs. Leading AI Models
No comparison table can tell you which model will make fewer mistakes in your private repository. It can clarify the purchase decision. The table below uses August 14 information and distinguishes list pricing from measured behavior.
| Model | Standard API price per 1M input/output tokens | Main strengths | Main limitations | Best fit |
|---|---|---|---|---|
| Grok 4.6 | $2 / $6 below 200K prompt; $4 / $12 at 200K+ | Excellent price-to-performance; strong agent and coding scores; 500K context; image input | High time to first token in independent testing; somewhat verbose; proprietary; long-context repricing | Cost-sensitive coding, research and multi-step agents |
| GPT-5.6 Sol | $5 / $30 headline rate | Strong coding and long-running professional work; beats Grok on DeepSWE and Terminal-Bench v3.0 in SpaceXAI’s table | Much higher output price; task cost depends on reasoning level and token use | Teams prioritizing high-end coding depth and OpenAI ecosystem integration |
| Claude Opus 5 | $5 / $25 headline rate | Highest independent composite score among models compared here; strong long-horizon quality | Premium price; can accumulate substantial context and turns on long tasks | Complex analysis and architecture where quality is worth the premium |
| Claude Fable 5 | $10 / $50 headline rate | Top-tier intelligence and strong coding/agent performance | Highest listed price in this group | High-value work where marginal quality matters more than token cost |
| Grok 4.5 | $2 / $6 below 200K prompt; $4 / $12 at 200K+ | Same base price and context size; lower cached-input rate | Lower scores across every benchmark in the Grok 4.6 launch table | Existing pinned workflows that need stability more than the newest model |
Pricing changes frequently and can vary by provider, service tier, context length, caching and promotions. Verify the provider’s current billing page before production use.
Why the cheapest model is not always the cheapest system
A completed task is the correct unit of comparison. If Model A costs half as much per token but needs twice as many repair attempts, there may be no saving. If Model B is more expensive but produces a mergeable patch on the first run, it may be the economical choice.
For an honest pilot, give every candidate the same 20 to 50 representative jobs. Track completion rate, human review minutes, regressions, total tokens, cache hits, tool calls, wall-clock time, and the percentage of outputs accepted without major edits. Do not choose a production model from a public leaderboard alone.
Where Grok 4.6 Could Be Most Useful
1. Repository-scale coding
The jump in DeepSWE, CursorBench, FrontierCode, and APEX-SWE suggests a stronger coding model than Grok 4.5. The practical opportunity is not autocomplete. It is multi-file work: tracing behavior across a codebase, planning a change, editing connected modules, running tests, and revising the patch.
Cursor availability makes experimentation easy for developers already working there. Start with bounded tickets that have clear tests. Avoid granting production credentials or broad deployment authority until the model has earned trust in your environment. For a broader tool comparison, see Cursor vs Lovable vs Bolt vs Replit: Best AI Tool 2026.
2. Long-form research and knowledge work
Grok 4.6’s GDPVal-AA and AA-Briefcase results point to a model that can assemble research, analysis, and polished work products across multiple steps. That could be useful for competitive reviews, internal reports, policy research, financial-model commentary, and document-heavy due diligence.
The February 1 knowledge cutoff is a hard boundary for built-in knowledge. Current research requires web search, X search, a private retrieval system, or supplied documents. Every external source should remain visible to the reviewer. A fluent report without traceable evidence is not finished work.
3. Rapid app prototypes and visual interfaces
SpaceXAI says Grok 4.6 produces better first passes for interactive and visual projects. This is a vendor claim, but it aligns with the model’s training emphasis on web development and its placement inside Cursor and Grok Build. It may be attractive to founders who want a working first version before refining the product with a designer and engineer.
The risk is the familiar “great demo, fragile product” gap. A polished interface can hide missing validation, inaccessible controls, insecure defaults, and untested edge cases. Treat the first pass as a prototype, not production evidence.
4. High-volume agent workflows
The combination of low short-context output pricing, caching, and strong agent scores could be valuable for repeated document processing, customer-support triage, QA, data extraction, and code maintenance. These are also the workloads where small reliability differences compound. A 2% failure rate across 10,000 jobs is 200 failures.
Use schema validation, deterministic checks, spending limits, retry caps, logging, and human review for high-impact actions. MusTrend’s guide How to Build an AI Agent in 2026: A Step-by-Step Guide for Beginners explains the broader workflow.
What Reddit Users Are Praising—and Questioning
Reddit is not a representative survey. Users self-select, launch-day threads attract enthusiasts and skeptics, and many comments are reactions to charts rather than completed tests. It is still useful for identifying the questions buyers are likely to ask.
We reviewed more than 30 comments across the August 12 Grok 4.6 benchmark discussion and adjacent Cursor and coding-agent threads. Four themes appeared repeatedly.
Praise: the value proposition is hard to ignore
Positive commenters focused on the $2/$6 rate, speed, and coding value. Several described recent Grok versions as practical “workhorse” models for routine tasks. In the Cursor discussion, some users said Grok handled most of their daily work, while reserving Claude or GPT-5.6 for the hardest portion. That is a realistic deployment pattern: use a cheaper capable model by default and escalate difficult cases.
Expectation: Grok may finally be closing the quality gap
Some users viewed the five-point independent index gain as evidence that SpaceXAI can now improve quickly between releases. Others expected the Cursor relationship to strengthen coding performance. The excitement was less about a single score than about trajectory—the belief that Grok is no longer merely a cheaper alternative.
Concern: per-token pricing can hide total cost
One of the most practical criticisms was that reasoning models can be “yappy.” Users asked for the cost of completing the benchmarks, not only the rate per token. Artificial Analysis partly answers that concern with its $0.84 measured cost per index task and token-use reporting. Production teams should apply the same discipline to their own logs.
Skepticism: benchmark strength is not the same as trust
Several commenters questioned whether the model was over-optimized for public evaluations, pointing especially to its weaker Terminal-Bench v3.0 result relative to GPT-5.6 Sol and Claude Fable 5. Others said the differences among frontier models are becoming difficult to notice in ordinary use.
A separate group raised objections tied to Elon Musk and Grok’s past behavior rather than this model’s technical results. That brand trust issue is commercially relevant. Enterprise adoption depends on governance, predictable behavior, data policies, auditability, and vendor confidence—not only price and intelligence.
Community signal: Developers are excited about cost and routine coding performance, but they want proof from real repositories, complete task costs, and consistent behavior before replacing their current premium model.
Grok 4.6 Pros and Cons
| Advantages | Limitations |
|---|---|
| 61 on the independently measured Artificial Analysis Intelligence Index | Composite benchmark parity does not guarantee parity on a specific workload |
| Strong gains over Grok 4.5 across the launch evaluation table | Does not lead every coding or terminal benchmark |
| $2/$6 short-context token rates | All-token repricing to $4/$12 once the prompt reaches 200K |
| $0.84 measured cost per Intelligence Index task | Real costs also include reasoning, cache behavior, tools, retries and review |
| 500K context and image input | Smaller context window than some million-token competitors |
| Available through Cursor, Grok Build, API and partners | Proprietary weights and provider-dependent behavior |
| Strong long-horizon efficiency in AA-Briefcase | High measured time to first token and somewhat verbose output |
Market Impact: The Frontier Price War Moves to Agents
Grok 4.6 is not the cheapest capable model in the market. Smaller and open-weight models can cost far less, and Reuters reported in early August that DeepSeek’s V4-Flash was dramatically cheaper per task in independent testing, though it also scored below the frontier leaders. The strategic difference is that Grok 4.6 is competing near the top of the intelligence chart while charging a mid-market rate.
That puts pressure on three groups.
Frontier labs must justify premium output prices with better completion rates, safety, latency, ecosystem integration, or performance on high-value tasks. OpenAI has already cut prices on smaller GPT-5.6 tiers, according to Reuters reporting from July 30. Grok 4.6 gives buyers another reason to route ordinary work away from the most expensive flagship.
AI coding companies gain leverage when several models can perform well. They can route jobs by complexity, negotiate lower inference costs, and offer users faster or cheaper modes. Cursor’s launch-day access also gives Grok a distribution channel directly inside the developer workflow.
Enterprise buyers can stop treating model selection as a permanent vendor decision. A routing layer can send routine tasks to Grok 4.6, escalate uncertain outputs to a premium model, and use a specialized or local model for sensitive work. The market is moving toward portfolios, not monopolies.
The next six months
Three developments will determine whether Grok 4.6 has lasting impact.
- Independent coding-agent results: Public model benchmarks are useful, but end-to-end agents depend on the harness, tools, prompts, sandbox, and retry policy. Cursor and Grok Build results on real repositories will matter more than launch charts.
- Reliability and safety evidence: SpaceXAI says Grok 4.6 received its broadest pre-deployment testing and improved safeguard calibration. Buyers need detailed, reproducible evidence about hallucination, tool misuse, prompt injection, data handling, and failure recovery.
- Effective cost under long context: The 200K threshold may be irrelevant for many jobs and important for exactly the longest agent runs. Context compaction and caching quality will determine how often users pay the doubled rate.
The likely outcome is not a universal migration to Grok. It is more competitive routing. Grok 4.6 is now credible enough to earn a place in a model bake-off, especially where output cost is a major constraint.
Should You Switch to Grok 4.6?
Try it now if you run high-volume coding, research, or agent workloads; already use Cursor or Grok Build; can evaluate outputs automatically; and want a frontier-capable model at a lower short-context rate.
Run a controlled pilot first if your sessions regularly approach 200,000 tokens, your work requires citations and current information, or your team needs predictable latency. Measure complete task economics, not only API rates.
Keep a premium alternative if you do difficult architectural work, safety-critical analysis, or tasks where a single failure costs far more than the model bill. SpaceXAI’s own table shows GPT-5.6 Sol and Claude Fable 5 ahead on some demanding coding and terminal tests.
Wait if governance, vendor trust, model transparency, or mature enterprise controls are your main requirements. Technical performance cannot answer those concerns by itself.
A sensible deployment is tiered: Grok 4.6 for routine and medium-complexity work, automated validation for every output, and escalation to another model or a human for low-confidence or high-impact cases. That approach captures the price advantage without pretending one model is best at everything.
Frequently Asked Questions
When was Grok 4.6 released?
SpaceXAI officially released Grok 4.6 on August 12, 2026. It became available through Cursor, Grok Build, the SpaceXAI API, and several infrastructure partners at launch.
How much does the Grok 4.6 API cost?
For prompts below 200,000 tokens, the official price is $2 per million input tokens, $0.50 per million cached input tokens, and $6 per million output tokens. At 200,000 prompt tokens or more, the rates become $4, $1, and $12 respectively, and the long-context rate applies to all tokens in the request.
Is Grok 4.6 better than GPT-5.6 Sol?
There is no universal winner. Both score 61 on the Artificial Analysis Intelligence Index at the compared reasoning settings. Grok 4.6 is much cheaper at its short-context list rate and leads some agent evaluations. GPT-5.6 Sol leads Grok 4.6 on DeepSWE v1.1 and Terminal-Bench v3.0 in SpaceXAI’s published table.
Is Grok 4.6 better than Claude?
Grok 4.6 offers a stronger price-to-performance case, but Claude Opus 5 and Claude Fable 5 remain slightly ahead on the independent composite index. Claude Fable 5 also leads several coding and agent benchmarks in SpaceXAI’s comparison. Test both on your own tasks.
Is Grok 4.6 good for coding?
The evidence is encouraging. It improved substantially over Grok 4.5 on DeepSWE, CursorBench, FrontierCode, APEX-SWE, and Terminal-Bench. It is available directly in Cursor and Grok Build. Repository-specific testing is still necessary before granting it broad permissions.
Does Grok 4.6 have real-time internet access?
Not from its built-in knowledge alone. The documented knowledge cutoff is February 1, 2026. Developers must enable web search, X search, or another retrieval source for later information. Search tools add separate usage charges.
What is the Grok 4.6 context window?
The official model documentation lists a 500,000-token context window. Remember that the API moves to doubled long-context rates when the prompt reaches 200,000 tokens.
Does Grok 4.6 support images?
Yes. The current documentation lists text and image input with text output. It is not described as an image-generation model; SpaceXAI uses separate Imagine models for image generation.
Is Grok 4.6 open source?
No. Grok 4.6 is a proprietary model, and SpaceXAI has not publicly disclosed its parameter count. Claims assigning it a specific model size should not be treated as confirmed unless SpaceXAI publishes that information.
Why can Grok 4.6 cost more than the $2/$6 headline?
Long-context requests, priority processing, server-side tools, reasoning tokens, retries, and verbose outputs can all increase the bill. The best metric is cost per accepted task after review.
Can Grok 4.6 replace a human developer or analyst?
No benchmark establishes that. It can accelerate bounded tasks, but humans still need to define requirements, review evidence, test code, protect credentials, and accept responsibility for high-impact decisions.
Conclusion: Grok 4.6 Earned a Trial, Not a Blank Check
Grok 4.6 is a meaningful release. It raises Grok’s independent Intelligence Index score from 56 to 61, strengthens performance across SpaceXAI’s coding and agent evaluations, and delivers a measured cost per benchmark task that puts it on the efficiency frontier. At short context, its $2/$6 pricing gives developers a serious alternative to much more expensive flagship models.
The strongest claim supported by the evidence is not “Grok 4.6 is objectively number one.” It is that Grok has returned to the frontier and may now offer the best value for certain multi-step workloads. Claude remains ahead on the independent composite score. GPT-5.6 Sol and Claude Fable 5 win some demanding coding tests. Grok’s initial latency, verbosity, long-context price threshold, and trust questions remain material.
If your AI bill is growing, this model deserves a controlled trial. Use real tasks. Record the full bill. Count human review time. Test failures, not only happy paths. The model that wins that experiment—not the model with the loudest launch chart—is the one worth deploying.
If this analysis helped you separate the benchmark headlines from the real buying decision, share it with a developer, founder, or team evaluating AI agents this month.
Related Reading on MusTrend
- AI API Cost Calculator (2026) – GPT, Claude & Gemini Pricing
- Meta Muse Code Is Here — Is It the Cheapest Serious AI Coding Agent Yet?
- Cursor vs Lovable vs Bolt vs Replit: Best AI Tool 2026
- How to Build an AI Agent in 2026: A Step-by-Step Guide for Beginners
- AI Agents Are Quietly Rewriting the North American Workday in 2026
Sources and Verification Notes
- SpaceXAI — Introducing Grok 4.6, August 12, 2026
- SpaceXAI developer documentation — Models, updated August 12, 2026
- SpaceXAI developer documentation — Pricing, checked August 14, 2026
- Artificial Analysis — Grok 4.6 benchmarks and analysis, August 12, 2026
- Artificial Analysis — Grok 4.6 performance and price profile, checked August 14, 2026
- 9to5Mac — SpaceXAI releases Grok 4.6, August 12, 2026
- Yahoo Finance / 24/7 Wall St. — SpaceX Just Unveiled Grok 4.6, August 12, 2026
- Reddit r/singularity — Grok 4.6 benchmark discussion, reviewed August 14, 2026
- Reddit r/cursor — Grok user experience discussion, reviewed August 14, 2026