Model comparison

Gemini 3.1 Pro vs Mistral Large 3

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

Catalog record checked September 5, 2026Individual provider fields may changeEvidence confidence: High — see receipts below

Google

Gemini 3.1 Pro

Frontier

Mistral

Mistral Large 3

Frontier · Open weights

AI model capability comparison
Specification	Gemini 3.1 Pro	Mistral Large 3
Provider	Google	Mistral
Tier	Frontier	Frontier
Context window	Winner: 1.05M	262K
Max output	66K	Winner: 262K
Input / 1M tokens	$2	Winner: $0.50
Output / 1M tokens	$12	Winner: $1.50
Weights	Closed	Open
Parameters	Not disclosedUnverified	675B total / 41B active (sparse MoE)
Reasoning levels	low, medium, high	Not verifiedUnverified
Modalities	text, image, video, audio, pdf	text, image
License	Not disclosedUnverified	Apache 2.0
API model id	gemini-3.1-pro-preview	mistral-large-3
Released	February 19, 2026	December 1, 2025
Artificial Analysis Intelligence Index (2026-08-14)	Winner: 60	46
SWE-bench Verified (2026-02)	80.6	Not verifiedUnverified
GPQA Diamond (2026-02)	Winner: 94.3	43.9
Humanity's Last Exam (2026-02)	44.4	Not verifiedUnverified
Terminal-Bench 2.1 (2026-07)	73.8	Not verifiedUnverified

Prices are USD per million tokens at standard rates, excluding batch and caching discounts. Bold indicates the better figure where one is objectively better. Values we could not confirm from the provider are shown as “Not verified” rather than estimated.

Benchmark receipts

2026-08-14: Artificial AnalysisComposite index blending reasoning, knowledge, and coding evals into one 0–100 score. Comparability: directly comparable — AA occasionally rebaselines the index scale between snapshots — a score captured on one date is only comparable to same-snapshot scores (check measuredAt).
2026-07: 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. Comparability: comparable with caveat — Scores across Terminal-Bench major versions (v1 vs v2) are NOT comparable; harness configuration (container, time limits) also shifts results.
2026-02: Google DeepMind model evaluation report (vendor, no tools; 51.4 with search+code)Real GitHub issue resolution: does the model's patch pass the hidden tests. Comparability: comparable with caveat — Post-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.
2025-12-02: Mistral — Introducing Mistral 3 (vendor, base model, 5-shot no CoT); independent review concursGraduate-level science reasoning: multiple-choice questions written by domain PhDs. Comparability: comparable with caveat — Small question pool: differences under ~2 points are within run-to-run noise. Chain-of-thought vs direct answering must match to compare.

Pricing tiers: Gemini 3.1 Pro: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. · Mistral Large 3: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05/MTok. Open weights (Apache 2.0), EU-hosted.

FrontierRecord checked September 5, 2026

Gemini 3.1 Pro

Gemini 3.1 Pro is Google's current flagship Pro model with record benchmark scores and a 1M-token context.

Best for

Very long documents
Multimodal reasoning
Frontier knowledge work

Watch out

Still in Preview; 3.5 Pro is 'coming soon' but unreleased as of 2026-08-29.

FrontierOpen weightsRecord checked September 5, 2026

Mistral Large 3

Mistral Large 3 is Mistral's flagship — 262K-context, multimodal, Apache-2.0 open-weight at $0.50/$1.50.

Best for

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

Watch out

Verify multimodal support and EU data-residency on your 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. These are standard-tier API rates, excluding batch and cache discounts.

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Evidence confidence: High

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.
3/5 core specs verified on both sides — Not published for at least one side: parameter count, reasoning levels.
2 shared named benchmarks with differing scores — Measured on: Artificial Analysis Intelligence Index, GPQA Diamond.
Verified within the last 90 days — Newest catalog check was 6 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.

Gemini 3.1 Pro: Google Cloud — Gemini 3.1 Pro (accessed 2026-08-29)
Gemini 3.1 Pro: TechCrunch — Gemini 3.1 Pro (accessed 2026-08-29)
Mistral Large 3: Mistral docs — mistral-large-2512 (675B total / 41B active, 256K) (accessed 2026-09-05)
Mistral Large 3: Mistral — Introducing Mistral 3 (sparse MoE parameters) (accessed 2026-09-05)

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. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. Mistral Large 3 has tiered pricing: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05/MTok. Open weights (Apache 2.0), EU-hosted.

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. Output tokens usually dominate a real bill, so weigh the output rate more heavily than the input rate. These are base rates; Gemini 3.1 Pro has tiered pricing: $2/$12 per MTok (≤200K); $4/$18 (>200K). Still 'Preview' as of 2026-08-29; 3.5 Pro not yet released. Mistral Large 3 has tiered pricing: Mistral first-party API $0.50/$1.50 per MTok; cached input $0.05/MTok. Open weights (Apache 2.0), EU-hosted.

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 sit in the frontier tier, so the choice usually comes down to price and context rather than capability. Gemini 3.1 Pro suits very long documents; Mistral Large 3 suits open-weight deployments.

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.
Measured capability: Gemini 3.1 Pro leads Artificial Analysis Intelligence Index 60 to 46 (measured 2026-08-14).
Deployment: Mistral Large 3 publishes weights you can self-host; the other is API-only.

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