Head-to-head
Mistral Large 3 vs Gemini 3 Pro: which AI model wins in 2026?
Mistral Large 3 ($1.50/1M out) and Gemini 3 Pro ($12/1M out (prompts ≤200K)) are two of the most-used AI models in 2026. Across 3 community votes, Gemini 3 Pro leads with 57% approval.
Quick verdict
On Reasoning, pick Gemini 3 Pro: the arena rates it 4.5/5 against 3/5 for Mistral Large 3. On budget, Mistral Large 3 wins: it starts at $1.50/1M out versus $12/1M out (prompts ≤200K) for Gemini 3 Pro.
Line-by-line comparison
Strengths and weaknesses
Mistral Large 3
- Apache 2.0 open weights with single-node deployment via FP8/NVFP4 quantization, despite 675B total parameters
- 256K context window, at the upper end for open-weight models, well suited to long-document RAG
- Aggressive flagship pricing at $0.50 in / $1.50 out per 1M tokens, roughly 3-4x cheaper than Western proprietary flagships
- Debuted #2 among open-source non-reasoning models on LMArena (Elo ~1418)
- Native multimodality (2.5B-parameter vision encoder) and 40+ native languages
- Developers on HN praise its strict formatting and instruction following plus production reliability
- Weak deep reasoning: GPQA Diamond ~44% vs high-70s for DeepSeek V3.2 and Kimi K2 Thinking; no reasoning variant at launch
- Trails GLM-4.6, Kimi K2 and DeepSeek on modern coding benchmarks (middling LiveCodeBench v6); HN devs place it a 'different weight class' below Gemini 3, GPT-5.1 and Claude Opus 4.5
- Hallucination-prone on factual QA (SimpleQA ~24%) with weak abstention tuning
- Measured output speed ~49 tok/s on Artificial Analysis, below the ~58 tok/s median for comparable models
- HN criticism that the architecture closely mirrors DeepSeek V3, raising doubts about original R&D
Gemini 3 Pro
- Topped LMArena at launch with a record 1501 Elo and scored 91.9% on GPQA Diamond, state of the art at release
- ARC-AGI-2 at 31.1%, roughly 6x Gemini 2.5 Pro (4.9%) and nearly double GPT-5.1 (17.6%) at the time
- Best-in-class multimodal understanding: 81% MMMU-Pro, 87.6% Video-MMMU, with a 1M-token context window
- Strong agentic coding: 76.2% SWE-bench Verified, 54.2% Terminal-Bench 2.0, 1487 Elo on WebDev Arena
- Undercut rivals on price at $2/$12 per 1M tokens, below Claude Sonnet-class pricing ($3/$15)
- Configurable thinking_level (low/medium/high) lets developers trade reasoning depth against latency and cost
- Overconfident hallucinations: on AA-Omniscience it gave a wrong answer 88% of the time instead of declining, vs 48% for Claude Sonnet 4.5 (the-decoder)
- Sycophancy widely reported by reviewers (Zvi Mowshowitz: 'vast intelligence with no spine'); needs tight system prompts
- Tool-calling reliability issues in agent stacks: devs reported tool outputs dumped into the chat thread and more scaffolding needed than OpenAI/Anthropic models
- Slow at high thinking level: time to first token measured around 30-60s on AI Studio despite ~130 tok/s output speed
- Retired: shut down on the Gemini API and AI Studio on March 9, 2026, with gemini-3-pro-preview now aliased to Gemini 3.1 Pro
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
The arena’s verdict on Mistral Large 3
Choose Mistral Large 3 if you want an open-weight, EU-governed flagship for multilingual RAG, long-document work, or self-hosted deployments: versus Mistral Large 2 (dense 123B, restrictive research license, $2/$6 API pricing) it is a clear upgrade in context, multimodality, licensing, and cost. Avoid it as your primary coding or deep-reasoning engine; DeepSeek V3.2, GLM-4.6, or proprietary frontier models score materially higher there. Treat it as a cheap, reliable workhorse rather than a frontier performer.
The arena’s verdict on Gemini 3 Pro
A landmark release that put Google back on top in late 2025, with a huge reasoning jump over Gemini 2.5 Pro and the best multimodal scores of its generation. As of mid-2026 there is no reason to choose it: Google shut it down on the API on March 9, 2026, and Gemini 3.1 Pro costs exactly the same while more than doubling ARC-AGI-2 performance (77.1% vs 31.1%). Teams on legacy deployments should migrate to 3.1 Pro, which the old model ID now points to anyway. Avoid it for hallucination-sensitive workloads unless you add grounding, a weakness reviewers flagged repeatedly.
What the crowd says
On Mistral Large 3
No verdicts yet. Be the first to speak.
On Gemini 3 Pro
“Confidently wrong is its worst mode. On AA-Omniscience it gave a wrong answer 88% of the time instead of declining. Add the sycophancy and you need a tight system prompt to trust it.”
“ARC-AGI-2 at 31% was about 6x Gemini 2.5 Pro and nearly double GPT-5.1 at the time. For visual-heavy work (81 MMMU-Pro) nothing else came close.”
“1501 Elo on LMArena at launch was deserved. Multimodal is where it kills, I feed it lecture videos and dense PDFs and it just gets it. 1M context helps.”
Keep comparing
Frequently asked questions
Is Mistral Large 3 better than Gemini 3 Pro?
The crowd currently sides with Gemini 3 Pro: 57% recommend it, versus 50% for Mistral Large 3 (3 votes). On Reasoning, Gemini 3 Pro rates higher (4.5/5 vs 3/5). The right pick depends on your use case. The line-by-line comparison on this page breaks down pricing, key specs and arena ratings.
Which is cheaper, Mistral Large 3 or Gemini 3 Pro?
Mistral Large 3 is cheaper: it starts at $1.50/1M out, while Gemini 3 Pro starts at $12/1M out (prompts ≤200K).
How much do Mistral Large 3 and Gemini 3 Pro cost per 1M tokens?
Mistral Large 3: $0.50/1M in per 1M input tokens, $1.50/1M out per 1M output tokens. Gemini 3 Pro: $2/1M in (prompts ≤200K) per 1M input tokens, $12/1M out (prompts ≤200K) per 1M output tokens.