Grok 4.7 scores 46 on the Artificial Analysis Intelligence Index to bring SpaceXAI into the top 4 AI labs. Coding Agent Index performance has also improved, overtaking GPT-5.6 Sol
Grok 4.7 scores +2 points over Grok 4.6 on the Intelligence Index, with strong performance on agentic knowledge work tasks. We evaluated the new model at xhigh reasoning effort.
Congratulations to @SpaceXAI and @ElonMusk on the release!
Key takeaways:
➤ Grok 4.7 joins the frontier of agentic knowledge work: Grok 4.7 gains +111 Elo over Grok 4.6 (high) on AA-Briefcase, our private benchmark for long-horizon agentic knowledge work, scoring 1657 Elo and placing it alongside Claude Opus 5 and Claude Fable 5.1 at the frontier. On GDPval-AA, it scores 1695 Elo, +90 ahead of Grok 4.6 (high).
➤ A leap in coding agent performance: Grok 4.7 (xhigh) with Grok Build scores 56 on the Artificial Analysis Coding Agent Index, up +9 points from Grok 4.6 (xhigh). Among models in their native harnesses, Grok 4.7 + Grok Build now ranks 4th, behind only Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5.
➤ Incremental performance changes elsewhere: Outside of agentic knowledge work, Grok 4.7 broadly matches Grok 4.6…
Grok 4.7 gains +111 Elo over Grok 4.6 on AA-Briefcase, Artificial Analysis's private long-horizon agentic knowledge-work benchmarkA standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.Full definition →, reaching 1657 Elo — putting it alongside Claude Opus 5 and Claude Fable 5.1 at the frontier.
With Grok Build, Grok 4.7 scores 56 on the Artificial Analysis Coding AI agentAn AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.Full definition → Index, up 9 points from Grok 4.6, ranking 4th among native-agent harnessThe scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.Full definition → combos behind Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5.
The gains come at a token cost: Grok 4.7 (xhigh) uses about 81k output tokens per Intelligence Index task, 125% more than Grok 4.6 (high) and 196% more than GPT-6 Astra (max).
Outside agentic knowledge work, Grok 4.7 mostly matches Grok 4.6, improving on Terminal-Bench 4.0 (+4.5 points) and GDP.pdf (+3.0 points) but regressing on AA-LCR (-3.7 points) and AutomationBench-AA (-1.1 points).
Pricing is unchanged from Grok 4.6 at $2/$6 per 1M input/output tokens, with cache-hit discounts to $0.50 per 1M tokens, and the context windowThe maximum amount of text a model can consider at once — its working memory for the current conversation or task.Full definition → stays at 500k tokens.
Terms in this piece · Glossary
AI agent — An AI system that doesn't just answer once but works toward a goal in a loop — taking actions, reading the results, and deciding what to do next.
agent harness — The scaffolding around a model that turns it into a working agent — the loop, the tools it can call, and the rules for when to stop.
benchmark — A standard public test set for comparing AI models — the shared scoreboards behind every "model X beats model Y" claim.
context window — The maximum amount of text a model can consider at once — its working memory for the current conversation or task.
Why it matters
Grok 4.7 gains +9 points on the Coding Agent Index, ranking 4th among native-harness models behind Claude Fable 5.1, GPT-6 Astra, and Claude Opus 5, giving engineers a data point for model selection.
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