OpenAI GPT-6 Astra Scores A Disappointing 61 On Artificial Analysis Intelligence Index, Same As GPT 5.6 Sol

OpenAI's GPT-6 Astra scored 61 on the Artificial Analysis Intelligence Index, identical to its predecessor GPT-5.6 Sol, despite a 2.5x price increase to $10/$50 per million tokens. The model trails Anthropic's Claude Fable 5.1 (66), Claude Opus 5 (63), and Meta's Muse Spark 1.3 (62), though it performs better on the Coding Agent Index with a score of 67.

OpenAI’s much-awaited Astra model hasn’t brought about any gains at all in the popular Artificial Analysis Intelligence Index. GPT-6 Astra, running at max reasoning effort, has landed on the benchmark with a score of just 61 — identical to the score its predecessor, GPT-5.6 Sol, put up when it launched https://officechai.com/ai/gpt-5-6-sol-places-second-right-behind-claude-fable-on-artificial-analysis-intelligence-index/ . For a model that was supposed to represent OpenAI’s next big leap, a flat line on the industry’s most closely watched intelligence benchmark is not the headline the company would have wanted. The 61 score places GPT-6 Astra in a three-way tie with GPT-5.6 Sol and xAI’s Grok 4.6 high , five points behind Anthropic’s Claude Fable 5.1, which currently tops the index at 66 https://officechai.com/ai/claude-fable-5-1-scores-tops-artificial-analysis-intelligence-index-with-score-of-66-beats-opus-5-by-3-points/ . It also trails Claude Opus 5 at 63 https://officechai.com/ai/claude-opus-5-becomes-top-model-in-the-world-on-artificial-analysis-intelligence-index-beats-fable-5/ and, more awkwardly for OpenAI, comes in behind Meta’s newly released Muse Spark 1.3, which scored 62. Claude Fable 5 also edges it out at 62. In an index where model generations typically fight for single-digit gains, GPT-6 Astra’s inability to move the needle at all over Sol is a rare outcome. Making It Up On Price, Not Score The stagnant score comes alongside a steep price increase. Artificial Analysis notes that GPT-6 Astra is priced at 2.5x GPT-5.6 Sol’s current rates, jumping from $4/$20 to $10/$50 per million input/output tokens, with the same 90% discount on cache reads and a 25% premium on cache writes carried over. GPT-6 Astra does use about 10% fewer output tokens than Sol at max effort, and it technically defines a new Pareto frontier for the Intelligence Index when plotted against output tokens per task. But that token efficiency isn’t nearly enough to offset the pricing jump — Artificial Analysis calculates that Astra ends up 75% more expensive per task than Sol for essentially the same intelligence score, leaving it sitting behind its predecessor on the Intelligence Index vs Cost per Task frontier. It’s a similar story to what played out when Claude Opus 4.8 topped the index at launch https://officechai.com/ai/claude-opus-4-8-tops-artificial-analysis-intelligence-index-edges-out-gpt-5-5-with-score-of-61-4/ and when GPT-5.5 briefly led the pack https://officechai.com/ai/gpt-5-5-tops-artificial-analysis-with-score-of-60-goes-clear-of-gemini-3-1-pro-and-claude-opus-4-7/ — pricing and efficiency have become just as central to these launches as the raw score itself, and this time the efficiency gains simply don’t cover the cost hike. A Much Better Story In Coding If the Intelligence Index is a wash, GPT-6 Astra’s performance on the Artificial Analysis Coding Agent Index tells a more flattering story. Running in the Codex harness, Astra scores 67 — roughly on par with Claude Opus 5 and Claude Fable 5 running in Claude Code, and with Muse Spark 1.3 in Muse Code. Claude Fable 5.1 leads that index outright at 70 https://officechai.com/ai/claude-fable-5-1-by-far-most-expensive-model-on-artificial-analysis-intelligence-index-costs-57-more-than-opus-5/ . The efficiency gains here are real and substantial. Artificial Analysis says GPT-6 Astra uses roughly a third of the tokens that GPT-5.6 Sol max needed in the Codex harness for a similar task, and about a fifth of what Claude Opus 5 xhigh uses. At max effort, Astra costs about the same as GPT-5.6 Sol max while scoring two points higher on the index, and on a per-task basis it comes in at less than half the cost of Claude Fable 5 for an equivalent score. That puts GPT-6 Astra on the Pareto frontier for the Coding Agent Index versus cost — a much stronger positioning than it manages on the Intelligence Index. Hallucinations Cut In Half, But Other Metrics Slip The other notable improvement is on AA-Omniscience, Artificial Analysis’s knowledge and hallucination benchmark. GPT-6 Astra’s hallucination rate drops from 92% for GPT-5.6 Sol down to 51% — a significant reduction — and Artificial Analysis is careful to point out this doesn’t come at the cost of accuracy, since Astra’s accuracy score actually improved by 4 points over Sol at the same time. On AA-Briefcase, Artificial Analysis’s own long-horizon knowledge-work evaluation that tests models on multi-week projects spanning thousands of source files, GPT-6 Astra gains roughly 80 Elo points over its predecessor, driven by higher rubric scores and a jump in Analytical Quality Elo. That gain comes with a trade-off, though: Presentation Quality Elo actually falls, and GPT-5.6 Sol max continues to lead every model on that specific sub-metric. Elsewhere, the picture is mixed at best. GPT-6 Astra gains 6 points on Humanity’s Last Exam, but that’s offset by an 80-Elo-point drop on GDPval-AA v2, the benchmark adapted from OpenAI’s own dataset measuring economically valuable work across 44 occupations. Artificial Analysis also flags 2-3 point regressions across τ³-Banking customer support , SciCode scientific Python problems , and AA-LCR long-context reasoning — a fairly broad spread of small step-backs for a model billed as a major upgrade. For a launch that OpenAI clearly wanted to frame as a decisive leap forward, GPT-6 Astra’s Artificial Analysis results end up reading as a coding and efficiency story wrapped around an Intelligence Index score that simply didn’t move.