Head-to-head
Mistral Large 3 vs DeepSeek-V4: which AI model wins in 2026?
Mistral Large 3 ($1.50/1M out) and DeepSeek-V4 ($0.87/1M out) are two of the most-used AI models in 2026. Across 3 community votes, DeepSeek-V4 leads with 57% approval.
Quick verdict
On Reasoning, pick DeepSeek-V4: the arena rates it 4.5/5 against 3/5 for Mistral Large 3. On budget, DeepSeek-V4 wins: it starts at $0.87/1M out versus $1.50/1M out for Mistral Large 3.
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
DeepSeek-V4
- 1M-token context window (8x the 128K of V3.2) with up to 384K output tokens, standard on the official API
- Aggressive pricing: $0.435/$0.87 per 1M tokens (V4-Pro), roughly 28.7x cheaper per output token than Claude Opus 4.8; cache-hit input drops to $0.003625/1M (over 99% discount)
- MIT-licensed open weights for both V4-Pro and V4-Flash on Hugging Face: commercial use, fine-tuning and redistribution allowed
- Open-source SOTA on agentic coding: 80.6 on SWE-bench Verified (Think Max config), tied with Gemini 3.1 Pro, plus Codeforces rating 3206 (~rank 23 vs humans)
- Ranks #3 of 93 on the Artificial Analysis Intelligence Index (score 44), well above the 25 average
- Sparse-attention stack cuts 1M-context inference to 27% of V3.2's FLOPs and 10% of its KV cache
- Intermittent malformed tool calls: function calls sometimes emitted as plain text in content instead of the tool_calls field (GitHub issue deepseek-ai #1244)
- Thinking mode breaks long multi-turn tool-call chains with 400 errors in agent frameworks (OpenClaw issue #72044, fix still incomplete)
- Developers report it fabricating nonexistent APIs in custom codebases and acting on hallucinated user input in agent loops
- Very verbose (180M eval output tokens vs 95M median) and mid-pack speed at 54.6 tok/s (#39/93), which erodes the low per-token price in practice
- Text only (no vision or audio) and still a preview: the official release planned for mid-July 2026 adds peak-hour pricing that doubles listed API rates during Beijing business hours
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 DeepSeek-V4
Choose DeepSeek-V4 if you want near-frontier reasoning and agentic coding at 3x to nearly 30x below Claude Opus or GPT-5.5 pricing, or if MIT-licensed weights for self-hosting and fine-tuning matter to you. It is a decisive upgrade over V3.2: 8x longer context, far cheaper long-context inference and stronger coding, and the legacy deepseek-chat/reasoner endpoints are deprecated on July 24, 2026 anyway. Avoid it for production agents that depend on rock-solid multi-turn tool calling, where users still report malformed tool calls and fabricated APIs, and for any vision or audio work since it is text only. Latency-sensitive apps should also test first, as its verbosity and mid-pack 54.6 tok/s output speed offset some of the cost advantage, and budget for the peak-hour price doubling arriving with the official mid-July release.
What the crowd says
On Mistral Large 3
No verdicts yet. Be the first to speak.
On DeepSeek-V4
“Tool calling is flaky. Function calls sometimes land as plain text instead of the tool_calls field, and thinking mode 400s on long multi-turn chains. Not agent-ready yet.”
“MIT license on both Pro and Flash weights is the real story. Fine-tune, redistribute, ship commercially, no lawyer needed. Plus 384K output tokens for long-doc generation.”
“MIT weights, 1M context, and output tokens roughly 29x cheaper than Opus 4.8. Cache hits make input basically free. Moved my bulk pipelines over and the bill collapsed.”
Keep comparing
Frequently asked questions
Is Mistral Large 3 better than DeepSeek-V4?
The crowd currently sides with DeepSeek-V4: 57% recommend it, versus 50% for Mistral Large 3 (3 votes). On Reasoning, DeepSeek-V4 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 DeepSeek-V4?
DeepSeek-V4 is cheaper: it starts at $0.87/1M out, while Mistral Large 3 starts at $1.50/1M out.
How much do Mistral Large 3 and DeepSeek-V4 cost per 1M tokens?
Mistral Large 3: $0.50/1M in per 1M input tokens, $1.50/1M out per 1M output tokens. DeepSeek-V4: $0.435/1M in (cache hit $0.003625) per 1M input tokens, $0.87/1M out per 1M output tokens.