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Model comparison

Gemini 3.1 Pro vs Mistral Large 3

Google against Mistral, compared on context, price, and attributed benchmark results.

Recorded profile verification October 2, 2026Individual provider fields may changeEvidence confidence: Medium — see receipts below

Google

Gemini 3.1 Pro

Frontier

vs

Mistral

Mistral Large 3

Frontier · Open weights

AI model capability comparison
SpecificationGemini 3.1 ProMistral Large 3
ProviderGoogleMistral
TierFrontierFrontier
Context windowWinner: 1.05M262K
Max output66KNot verifiedUnverified
Input / 1M tokens$2Winner: $0.50
Output / 1M tokens$12Winner: $1.50
WeightsClosedOpen
ParametersNot verifiedUnverified675B total / 41B active (sparse MoE)
Reasoning levelslow, medium, highNot verifiedUnverified
Modalitiestext, image, video, audio, pdftext, image
LicenseNot verifiedUnverifiedApache 2.0
API model idgemini-3.1-pro-previewmistral-large-2512
ReleasedFebruary 19, 2026December 2, 2025
SWE-bench Verified · source date Gemini 3.1 Pro: 2026-0280.6Not verifiedUnverified
GPQA Diamond · source date Gemini 3.1 Pro: 2026-02, Mistral Large 3: 2026-09-26 (comparability not verified)94.368
Humanity's Last Exam · source date Gemini 3.1 Pro: 2026-0244.4Not verifiedUnverified
Terminal-Bench 2.1 · source date Gemini 3.1 Pro: 2026-0773.8Not verifiedUnverified
Artificial Analysis Intelligence Index · source date Mistral Large 3: 2026-10-01Not verifiedUnverified9

Dates beside benchmark scores refer to the linked source’s publication or capture date unless that source explicitly states when it measured the result. A shared URL alone does not establish comparable runs; unverified setups are marked. Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

Where each score comes from, and how far it can be compared across models.

  • Source date2026-10-01
    Artificial Analysis (Mistral Large 3)

    Composite index blending reasoning, knowledge, and coding evals into one 0–100 score.

    Comparable with caveatOnly same-version scores are comparable. v4.3.2 was rebaselined (the top score fell from 66 on v4.1.1 to ~58), so v4.1.1 figures must not be compared with v4.3.2 figures — check the cited source and its date.

  • Source date2026-09-26
    Artificial Analysis GPQA Diamond (independent; replaces Mistral's non-comparable base-model 5-shot 43.9)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-07
    Google DeepMind model evaluation report (vendor, Terminus-2; own Feb report separately reported TB 2.0 = 68.5 — versions preserved)

    Agentic terminal work: multi-step tasks executed in a sandboxed shell environment.

    Comparable with caveatNot comparable with Terminal-Bench 3.0 or 4.0 (different task sets) or v1; harness configuration (container, time limits) also shifts results.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)

    Frontier-knowledge ceiling: extremely hard multi-domain questions written to be near-impossible without deep expertise.

    Comparable with caveatSubset/tool configurations (text-only vs with-tools) differ between vendors and materially change scores; must match configuration to compare.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, no tools, pass@1)

    Graduate-level science reasoning: multiple-choice questions written by domain PhDs.

    Comparable with caveatDo not treat a fixed two-point band as universal measurement uncertainty; it depends on run count and evaluation setup. Chain-of-thought, prompt, and tool policy must match. The small pool limits fine-grained conclusions, and frontier scores can be near ceiling; Artificial Analysis dropped it from its Intelligence Index in v4.3.

  • Source date2026-02
    Google DeepMind model evaluation report (vendor, Thinking High, avg of 10 runs, +0.6 disclosed adjustment)

    Real GitHub issue resolution: does the model's patch pass the hidden tests.

    Comparable with caveatPost-audit vendor claims and pre-audit scores sit on different task trust levels; scaffolding (agent harness, compute budget) also dominates results. Never aggregate across scaffolds.

Pricing tiers

Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable.

Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.

FrontierRecord checked October 1, 2026

Gemini 3.1 Pro

Gemini 3.1 Pro Preview is Google's multimodal Pro endpoint with a 1,048,576-token input limit and 65,536-token output limit.

Best for

  • Very long documents
  • Multimodal reasoning
  • Frontier knowledge work

Watch out

This API endpoint remains Preview. A separate customtools endpoint is optimized for bash/custom-tool workflows and can behave differently on other tasks. Keep endpoint, thinking level and benchmark version matched; restricted Gemini 4 Argon access is a separate lifecycle.

FrontierOpen weightsRecord checked October 2, 2026

Mistral Large 3

Mistral's December 2025 multimodal 675B/41B sparse MoE with a 256K context; Medium 3.5 is a separate dense model with configurable reasoning.

Best for

  • Open-weight deployments
  • Multimodal EU-hosted inference
  • Balanced frontier work

Watch out

The publisher describes Large 3 as not a dedicated reasoning model and documents vision limitations. It advertises FP8 deployment on a B200/H200 node or a separate NVFP4 checkpoint on H100/A100 nodes; this is not a guarantee for every serving setup. A separate hard output cap is not verified. Confirm regional residency on the actual endpoint.

When the cheaper one wins

Mistral Large 3 is cheaper on output at $1.50 per million tokens against $12 for Gemini 3.1 Pro — about 8.0×. Use the cheaper tier for classification, extraction, summarisation, and any task where the expensive model’s extra score does not change the accepted output. The expensive one only pays if your hardest task actually fails on the cheap tier. This compares standard short-context token rates, not total cost per accepted task. Include input, cache, context tiers, schedules, retries and review in your pilot.

Run the model picker

Evidence confidence: Medium

How strong and complete the evidence behind this comparison is — not a prediction of which model is better.

  • Pricing verified on both sides — Input and output rates are verified for both models.
  • 2/5 core specs verified on both sides — Not published for at least one side: max output, parameter count, reasoning levels.
  • No directly aligned benchmark setup — No benchmark has matching cited sources, source dates, units, effort and recorded run details on both models.
  • Verified within the last 90 days — Older of the two catalog checks was 25 days ago.
  • Both models carry source citations — Each side has at least two catalog sources on record.

Source receipts

Each catalog figure was checked against the provider or an independent second source on the date shown.

Related comparisons

Diving deeper on one model? Gemini 3.1 Pro · Mistral Large 3

Common questions

Gemini 3.1 Pro vs Mistral Large 3

Answered from the verified figures on this page rather than general guidance.

Is Gemini 3.1 Pro or Mistral Large 3 cheaper for input?
Mistral Large 3 is cheaper at $0.50 per million input tokens, against $2 for Gemini 3.1 Pro — roughly 4.0× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.
Is Gemini 3.1 Pro or Mistral Large 3 cheaper for output?
Mistral Large 3 is cheaper at $1.50 per million output tokens, against $12 for Gemini 3.1 Pro — roughly 8.0× the price. Estimate both input and output token volume for your workload. These are base rates; Gemini 3.1 Pro: The current Gemini developer guide lists $2/$12 per MTok below 200K input and $4/$18 above 200K, including thinking output. It leaves the exact 200K boundary unspecified; confirm that boundary and other processing/cache tiers before budgeting. The exact API ID remains gemini-3.1-pro-preview. Restricted Gemini 4 Argon is a separate preview and does not make this endpoint stable. Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05. The 675B-total / 41B-active multimodal checkpoint declares Apache 2.0. Regional hosting is endpoint-specific.
Which has the larger context window, Gemini 3.1 Pro or Mistral Large 3?
Gemini 3.1 Pro accepts 1.05M tokens against 262K for Mistral Large 3. This only matters if you routinely send very long documents or large codebases.
Should I use Gemini 3.1 Pro or Mistral Large 3?
Both are classified in our frontier tier; this editorial grouping does not imply equal capability. Gemini 3.1 Pro is a candidate for very long documents; Mistral Large 3 is a candidate for open-weight deployments. Compare accuracy, tool reliability, latency and cost on representative tasks.
Can I self-host Gemini 3.1 Pro or Mistral Large 3?
Mistral Large 3 publishes open weights, but self-hosting and third-party provider access depend on the licence, hardware, serving support, and availability. Gemini 3.1 Pro is a closed model whose supported access paths are controlled by its provider.

Next step

Choosing between them

The verified figures that separate this pair, computed from the catalog rather than restated boilerplate.

  • Input price: Mistral Large 3 costs $0.50 per 1M tokens versus $2 for Gemini 3.1 Pro — a 4x difference at the headline tier.
  • Context: Gemini 3.1 Pro takes 1.05M against 262K for Mistral Large 3 — only decisive if your prompts approach the smaller window.
  • Deployment: Mistral Large 3 publishes weights you can self-host; the other is API-only.
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