Head-to-head
Mistral Large 3 vs Claude Opus 4.5: which AI model wins in 2026?
Mistral Large 3 ($1.50/1M out) and Claude Opus 4.5 ($25/1M out) are two of the most-used AI models in 2026. Compare them line by line below, then cast your verdict.
Quick verdict
On Reasoning, pick Claude Opus 4.5: the arena rates it 3.5/5 against 3/5 for Mistral Large 3. On budget, Mistral Large 3 wins: it starts at $1.50/1M out versus $25/1M out for Claude Opus 4.5.
Line-by-line comparison
From
$1.50/1M outSingle API tier on La Plateforme ($0.50 in / $1.50 out per 1M tokens), with a 50% discount via the batch API; Apache 2.0 weights allow free self-hosting, and third-party host pricing may differ.
$25/1M outOfficial Anthropic API list price for claude-opus-4-5 (single tier, no long-context premium; 200K context, 64K max output); same $5/$25 rate as its Opus 4.6-4.8 successors; 50% batch discount and prompt caching apply. Verified against platform.claude.com models overview 2026-07.
Provider
Mistral AI
Anthropic
Context window
256K tokens
200K tokens
Input price
$0.50/1M in
$5/1M in
Output price
$1.50/1M out
$25/1M out
Modalities
text, vision (image input), text output
text, vision
Open weights
Yes
No
Crowd score
50%(0)
50%(0)
Arena ratings (1-5)
Reasoning
3.0
3.5
Coding
3.0
3.5
Writing
4.0
3.5
Speed
3.5
2.5
Value
4.0
2.5
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
Claude Opus 4.5
- First model past 80% on SWE-bench Verified (80.9% at launch), beating Gemini 3 Pro and GPT-5.1 on real-world coding
- 66% price cut vs Opus 4.1 ($5/$25 vs $15/$75 per 1M tokens) made Opus-tier viable for production workloads
- 48-76% fewer output tokens than Sonnet 4.5 at matched or better quality, compounding the price cut
- Effort parameter (introduced with this model) lets devs trade reasoning depth for cost and latency per call
- Strong hands-on reports: one-shot complex refactors, caught race conditions other models missed, converged in ~4 agentic iterations vs ~10 for rivals
- +29% on Vending-Bench vs Sonnet 4.5, with fewer dead-ends on long-horizon autonomous tasks
- 200K context window only (64K max output), far behind the 1M of Gemini 3 Pro and later Claude models; users reported selective attention above ~70% context fill
- Gated to $100-200/month Max tiers in Claude apps at launch; Pro subscribers were locked out and heavy users still hit limits (HN called it 'penny-wise and pound-foolish')
- Moderate latency; extended thinking adds cost and delay on simple tasks
- Superseded since early 2026: Opus 4.6/4.7/4.8 cost the same $5/$25 with 1M context and higher benchmarks, leaving 4.5 no price advantage
- Legacy API surface: manual budget_tokens extended thinking rather than the adaptive thinking of newer Claude models
Cast your verdict
One recommendation per tool per gladiator. It reshapes the crowd score everyone sees.
Mistral Large 3$1.50/1M out
50%crowd score · 0
Claude Opus 4.5$25/1M out
50%crowd score · 0
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 Claude Opus 4.5
Pick Claude Opus 4.5 only if you have a workload already tuned and pinned to this snapshot (claude-opus-4-5-20251101) and need stability. It was a landmark release, the first past 80% on SWE-bench Verified and a 66% price cut over Opus 4.1, but Anthropic now sells Opus 4.6 through 4.8 at the identical $5/$25 rate with a 1M context window and better scores. Anyone starting a new project should choose Opus 4.8 instead, and cost-sensitive users get near-Opus coding from Sonnet 5 at $3/$15 (intro $2/$10 through Aug 2026). Avoid it entirely if your prompts approach the 200K context ceiling.
Keep comparing
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Frequently asked questions
Is Mistral Large 3 better than Claude Opus 4.5?
On Reasoning, Claude Opus 4.5 rates higher (3.5/5 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 Claude Opus 4.5?
Mistral Large 3 is cheaper: it starts at $1.50/1M out, while Claude Opus 4.5 starts at $25/1M out.
How much do Mistral Large 3 and Claude Opus 4.5 cost per 1M tokens?
Mistral Large 3: $0.50/1M in per 1M input tokens, $1.50/1M out per 1M output tokens. Claude Opus 4.5: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.