Mistral Large 4 vs GPT-6 Astra: Can a 1-Trillion-Parameter Open Model Compete?

On Artificial Analysis's Intelligence Index v4.3.2, Mistral Large 4 Preview scores 38 and GPT-6 Astra (Max) scores 53, as captured on 13 October 2026. Mistral says Large 4 is a 1-trillion-parameter, open-weight model, but as of that date it is a hosted preview, and the weights are promised for the end of October. The independent gap is large; the cost gap runs the other way, at $1.13 per task for Mistral against $3.26 for Astra.

Is Mistral Large 4 actually open yet?

No. Mistral released Large 4 on 6 October 2026 as a "Public Preview" on its API platform, and its launch post says "Weights drop end of this month." The model card and model list label it "Open" but name no license, whereas the same list names Apache 2.0 for Mistral Small 4 and Modified MIT for Mistral Medium 3.5.

That matters for the title question. "Open model" is a statement of intent today, not something you can download. Until the license is published, nobody can say whether self-hosting, fine-tuning or commercial redistribution is allowed. Artificial Analysis's own page marks the open-weights field "No" for both models.

What is inside the 1-trillion-parameter model?

Mistral's model card lists 1.05T total parameters, 52B active parameters and a 1.6B vision encoder, in a granular Mixture-of-Experts design. The launch post rounds this to "1 trillion" and says the model was trained from scratch on 3,800 NVIDIA Grace Blackwell GPUs in Mistral's own European datacenters. All of these are company-reported figures.

Two details differ across sources. Artificial Analysis and several outlets give 49B active parameters, while Mistral's own pages say 52B; this article uses Mistral's figure. The context window is 1M tokens on the model card but 524k on Artificial Analysis's spec table. GPT-6 Astra's parameter count is not disclosed, so no size comparison is possible.

Prices are list prices as of 13 October 2026. Mistral's pricing page also shows a "Sale price" of $0.68 input and $2.09 output with no end date, so the list price is the safer planning number. Astra's price matches The Next Web's report of $10 and $50.

How do they score on independent benchmarks?

Astra leads on nearly every test in Artificial Analysis's comparison, which is independent and uses one harness for both models. The page names Astra at its "Max" setting and gives no effort label for Mistral. It shows no capture date, so the values below are as fetched on 13 October 2026.

SciCode and CritPt are marked "Under review" on the page, and GDPval-AA v2.1 and AA-Briefcase v1.1 are marked "Updated". Treat those four rows as provisional. Index numbers are specific to v4.3.2 and should not be compared with scores from other index versions.

The one tie is long-context reasoning, where both score 81% on AA-LCR v1.1. For background on why Terminal-Bench numbers move between versions, see Terminal-Bench 4.0 explained.

What does Mistral claim, and how does it compare?

Mistral's launch post makes self-reported claims that mostly use different benchmarks, so they cannot be set against Astra directly. The figures below are all lab-reported, and the text gives no harness or reasoning effort for them.

Where an independent number exists, it lands within about one point of Mistral's own, which is a good sign for the company's reporting. The Terminal-Bench figure is the useful contrast: Mistral's 28.3% and Artificial Analysis's 27% agree, but both sit far below Astra's 59% in that harness. OpenAI's own figure for Astra is 57.9%, so the lab-reported and independent Astra numbers differ by about one point in the other direction. A larger harness gap on a different model is covered in Claude Opus 5.5 on Terminal-Bench 4.0.

Mistral also cites a Surge AI blind human coding evaluation on a 1 to 5 scale, where Large 4 Preview scores 3.74, second of five models, behind Claude Opus 5 at 4.22. Astra is not listed among the models in the text of that comparison.

Does the cyber claim hold up?

It is the least comparable claim in the post. Mistral says Large 4 scores 82% on a vulnerability reproduce-and-patch test within Artificial Analysis's Cyber Index and 93% on Cybench, a set of 40 exercises. It adds that Claude Opus 5.5 and GPT-6 Astra "score near zero on the same test because they refuse to perform the task."

If that is right, the result measures refusal policy as much as capability. A model that declines the prompt scores zero regardless of what it could do. Astra is also the model that, per The Next Web, OpenAI rates at its "Critical" cyber threshold, so refusals on offensive tasks are expected behaviour. No independent reproduction of Mistral's cyber numbers is published, and Artificial Analysis's comparison page shows no Cyber Index figure for either model.

What does each model cost per task?

Per task, the gap is roughly 3x rather than the 7x to 12x that list prices suggest. Artificial Analysis measured $1.13 per task for Mistral and $3.26 for Astra (Max), and $1,607 against $5,324 to run the full Intelligence Index.

Mistral is cheap per token but spends about 2.6 times as many output tokens per task, which eats most of the saving. The blended price uses a 7:2:1 cache-hit, input and output ratio, so it flatters any model with cheap cached input. The same effect appears in Gemini 4 Argon's cost per task. If you price a workload, use cost per task, not the per-token list.

So, can it compete?

On general capability measured independently, not yet: a 15-point Intelligence Index gap and a 32-point Terminal-Bench gap are not close. On cost per task and on a few narrow tests, it is competitive. The sensible reading is "strong open-weight candidate for cost-sensitive workloads that tolerate lower agentic accuracy," not "frontier peer."

Pick Astra when agentic coding, hard reasoning or knowledge accuracy decides the outcome. AA-Omniscience at -5 against 43 is a warning for factual work. Astra's own reliability profile is a separate question; see Gemini 4 Argon vs GPT-6 Astra on hallucination. Pick Mistral if the open-weight promise is the point, which means waiting for the license.

What's still unclear?

Several things could change this picture before the end of October 2026.

• License terms. No license is published, so the practical meaning of "open" is unknown.
• Preview versus final. Artificial Analysis scored a "Preview" model. Mistral says the reinforcement learning run behind it is still in flight, so final weights may score differently.
• Active parameters and context window. Mistral says 52B active and 1M context; Artificial Analysis lists 49B and 524k.
• Effort settings. Artificial Analysis labels Astra "Max" and gives Mistral no effort label. Mistral's post does not say which Astra setting it compared against on Dense 200.
• Astra's lab-reported numbers. The Next Web reports that none of OpenAI's figures had been independently reproduced and that OpenAI compared Astra only with its own earlier model.- Cyber results. The 82% and 93% figures are Mistral's alone, and refusal behaviour confounds them.

The weakest point of the "open model competes" argument is the independent gap itself. Self-reported results on Mistral's chosen benchmarks are good; the head-to-head on a shared harness is not.

Humanbit, a data labelling and evaluation company, is affiliated with this site.