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Mistral Large 3

mistralmistral/mistral-large-3

Mistral's flagship 675B parameter model. Top-tier reasoning, coding, and multilingual capabilities with vision support.

Mistral Large 3, released December 2 2025 as part of the Mistral 3 family, is Mistral AI's flagship sparse mixture-of-experts (MoE) model with 675B total parameters but only 41B active per token. It delivers frontier-level performance across general knowledge, coding, mathematics, and multilingual tasks while maintaining exceptional cost-efficiency β€” it can run on a single 8Γ—GPU node via NVFP4 quantization. All models in the Mistral 3 family are released under the Apache 2.0 license, making Large 3 the most capable fully open-weight frontier model at the time of its launch.

From a TheRouter operator perspective, Large 3 sits alongside Mistral's smaller Ministral 3 models and its reasoning-enhanced Magistral family. Its sparse MoE architecture (41B active / 675B total) makes it a strong default for open-weight self-hosting scenarios where per-token efficiency matters more than peak single-query reasoning depth. For tasks requiring the hardest multi-step reasoning, Mistral's own Magistral Small (24B, reasoning-enhanced) or Anthropic/OpenAI frontier models may be more appropriate β€” Large 3 scores ~43.9% on GPQA Diamond vs Claude's 65% β€” but for high-volume production workloads, large document processing, and multilingual agent applications, Large 3's combination of open weights, 256K context, and $0.50/$1.50 pricing through TheRouter is hard to beat.

Best for
  • β€’ Open-weight, self-hosted production deployments where data sovereignty (GDPR) and zero vendor lock-in are critical β€” Large 3 is Apache 2.0 and deployable on-premises
  • β€’ High-volume multilingual workloads across 40+ native languages β€” Large 3's multilingual MMLU (85.5%, 8-lang) is best-in-class among open-weight models
  • β€’ Coding and code review at scale β€” 92% pass@1 on HumanEval matches leading proprietary models
  • β€’ Long-context document processing (256K context) β€” entire codebases, legal contracts, financial reports, and research papers in a single inference pass
  • β€’ Cost-sensitive production pipelines β€” 60-80% cheaper than GPT-4o or Claude Opus via TheRouter pricing, with per-token efficiency from the MoE 41B active design
Reach for something else if
  • β€’ Hardest multi-step reasoning (GPQA Diamond ~43.9%) β€” route to Claude Opus 4.7, Gemini 2.5 Pro, or Magistral Small instead
  • β€’ Complex vision tasks β€” multimodal support is newer and may lag behind GPT-4 Vision or Gemini on edge cases
  • β€’ Very low volume (under 1M tokens/month) β€” API alternatives may be simpler and cheaper at minimal scale without the overhead of self-hosting
  • β€’ Edge / consumer-hardware-only deployments β€” Large 3 requires 8-16 H100 GPUs; use Ministral 3B/8B for edge inference
Context Length
128K
Max Output
16K
Input Priceper 1M tokens
$0.540/ 1M tokens
Output Priceper 1M tokens
$1.62/ 1M tokens

Modalities

textimage→text

Capabilities

Vision

Pricing Breakdown

TypeRate
Input$0.540 / 1M tokens
Output$1.62 / 1M tokens

Supported Parameters

temperaturemax_tokenstop_ptoolstool_choiceresponse_formatstop

Specifications

ArchitectureSparse Mixture-of-Experts (MoE) β€” 41B active parameters out of 675B totaldocs.mistral.ai β†—verified
Context window256K tokens (standard-models.yaml reflects TheRouter route's offered context)docs.mistral.ai β†—verified
LicenseApache 2.0 (fully open-weight)mistral.ai β†—verified
Training infrastructureTrained from scratch on ~3,000 NVIDIA H200 GPUsmistral.ai β†—verified
Languages40+ native languages; best-in-class multilingual among open-weight modelswww.digitalapplied.com β†—to verify
Quantization / self-host efficiencyNVFP4 quantization and Blackwell-optimised kernels enable deployment on a single 8Γ—GPU node via vLLMmistral.ai β†—verified

Benchmarks

BenchmarkDistributionScoreSource
MMLU (8-language)
85.5%mistral.ai β†—
HumanEval (Pass@1)
92.0%www.digitalapplied.com β†—
MATH-500
93.6%www.digitalapplied.com β†—
MMLU-Pro
73.1%www.digitalapplied.com β†—
GPQA Diamond
43.9%www.digitalapplied.com β†—
LMArena (open-source non-reasoning)
#2 among open-source non-reasoning models (behind Gemini 3 Pro)mistral.ai β†—

API Usage Examples

Use the global api.therouter.ai endpoint shown below for new integrations; the legacy China accelerated endpoint is retired.

cURL
curl https://api.therouter.ai/v1/chat/completions   -H "Content-Type: application/json"   -H "Authorization: Bearer $THE_ROUTER_API_KEY"   -d '{
    "model": "mistral/mistral-large-3",
    "messages": [
      {"role": "user", "content": "Summarize the key points from this input."}
    ]
  }'

Chat completion

Standard chat through TheRouter's OpenAI-compatible surface. Mistral Large 3 is a general-purpose chat model; the OpenAI SDK works with only a baseURL swap.

cURL
curl https://api.therouter.ai/v1/chat/completions \
  -H "Authorization: Bearer $THEROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistral/mistral-large-3",
    "messages": [{"role": "user", "content": "Explain sparse MoE architectures to a junior developer."}]
  }'

More from mistral

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News & changes

2025-12-02

Mistral AI releases Mistral 3 family β€” Mistral Large 3 (675B MoE) under Apache 2.0

Mistral AI launched the Mistral 3 model family: flagship Mistral Large 3 (675B total / 41B active parameters, sparse MoE), plus Ministral 3B, 8B, and 14B models. All released under Apache 2.0. Large 3 was trained from scratch on ~3,000 H200 GPUs and debuted at #2 open-source non-reasoning model on LMArena. Key benchmark numbers: 85.5% MMLU (8-lang), 92% HumanEval, 93.6% MATH-500. Native vision support and 256K context window. Mistral also announced a multi-year cloud deal with HSBC.

re-authored by TheRoutermistral.ai/news β†—
2025-12-02

Mistral Large 3 model card published on docs.mistral.ai and Hugging Face

Mistral published the official model card detailing the 675B MoE architecture, 41B active parameters, training on 3,000 H200 GPUs, 256K context, vision capabilities, Apache 2.0 license, and API pricing at $2/$6 per M tokens on La Plateforme. The model card also confirmed NVFP4 quantization support enabling single 8xGPU node deployment.

re-authored by TheRouterdocs.mistral.ai β†—

Frequently asked

What is the difference between Mistral Large 3 (675B) and the smaller Mistral models?

Large 3 is Mistral's flagship sparse MoE model with 675B total / 41B active parameters. It delivers frontier performance with a 256K context window and native vision. The Ministral 3 models (3B/8B/14B) are dense, smaller models suitable for edge deployment and high-throughput simple tasks. Magistral Small (24B) adds [THINK]/[/THINK] reasoning tokens for deeper multi-step reasoning. Devstral-2 (123B) is specialised for code. Choose Large 3 for maximum capability at the best open-weight cost-to-performance ratio.

Can I self-host Mistral Large 3?

Yes. Weights are published on Hugging Face under Apache 2.0. NVFP4 quantization and Blackwell-optimised kernels enable deployment on a single 8xGPU node via vLLM or SGLang. Tensor parallelism is recommended per the model card. For most teams, the practical entry point is vLLM on FP8.

What is the licensing for Mistral Large 3?

Mistral Large 3 and all Mistral 3 models are released under the Apache 2.0 license. This allows free commercial use, modification, and redistribution. No usage fees, no rate limits, no data leaving your premises when self-hosted.

Does Mistral Large 3 support image input?

Yes β€” Large 3 has native vision support as confirmed by the model card and YAML entry (text + image input modalities). It processes images natively in the chat context. For image generation, route to a dedicated image model.

How does Mistral Large 3's context compare to the YAML value (128K) vs the 256K official spec?

Official Mistral docs specify 256K tokens as the native context window. The standard-models.yaml entry for the TheRouter route lists 128K β€” this reflects the context offered by the TheRouter upstream provider configuration for this model, not the model's native capability. Check the TheRouter dashboard for the latest offered context or configure a compatible upstream provider for the full 256K native window.

Fact ledger β€” every claim on this page traces here
sourceURLretrieved
Architecturedocs.mistral.ai β†—2026-05-28verified
Context windowdocs.mistral.ai β†—2026-05-28verified
Licensemistral.ai β†—2026-05-28verified
Training infrastructuremistral.ai β†—2026-05-28verified
Languageswww.digitalapplied.com β†—2026-05-28to verify
Quantization / self-host efficiencymistral.ai β†—2026-05-28verified
MMLU (8-language)mistral.ai β†—2026-05-28verified
HumanEval (Pass@1)www.digitalapplied.com β†—2026-05-28verified
MATH-500www.digitalapplied.com β†—2026-05-28verified
MMLU-Prowww.digitalapplied.com β†—2026-05-28to verify
GPQA Diamondwww.digitalapplied.com β†—2026-05-28to verify
LMArena (open-source non-reasoning)mistral.ai β†—2026-05-28verified
Mistral AI releases Mistral 3 family β€” Mistral Large 3 (675B MoE) under Apache 2.0mistral.ai/news β†—2026-05-28verified
Mistral Large 3 model card published on docs.mistral.ai and Hugging Facedocs.mistral.ai β†—2026-05-28verified
What is the difference between Mistral Large 3 (675B) and the smaller Mistral models?mistral.ai β†—2026-05-28to verify
Can I self-host Mistral Large 3?huggingface.co β†—2026-05-28to verify
What is the licensing for Mistral Large 3?mistral.ai β†—2026-05-28to verify
Does Mistral Large 3 support image input?docs.mistral.ai β†—2026-05-28to verify
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