Head-to-head
Mistral Large 3 vs Claude Opus 4.8: which AI model wins in 2026?
Mistral Large 3 ($1.50/1M out) and Claude Opus 4.8 ($25/1M out) are two of the most-used AI models in 2026. Across 3 community votes, Claude Opus 4.8 leads with 57% approval.
Quick verdict
On Reasoning, pick Claude Opus 4.8: 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 $25/1M out for Claude Opus 4.8.
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
Claude Opus 4.8
- SWE-Bench Pro 69.2% (vs 64.3% for Opus 4.7) and beats prior Opus models on CursorBench at every effort level; strong real-world reports on large refactors and multi-file bug hunts
- About 4x less likely than Opus 4.7 to let flaws in its own generated code pass unflagged; big jump on math reasoning (USAMO 2026: 96.7% vs 69.3%)
- 1M-token context and 128K output at unchanged $5/$25 pricing, with no long-context premium; batch API at 50% off ($2.50/$12.50)
- Fast mode (research preview) delivers up to 2.5x output speed at $10/$50, 3x cheaper than Opus 4.7's fast tier ($30/$150)
- Unique API features for agents: mid-conversation system messages that preserve the prompt cache, and Dynamic Workflows spawning parallel subagents in Claude Code
- 84% on Online-Mind2Web browser automation and record score on Legal Agent Benchmark (first model past 10% all-pass); strong enterprise knowledge work (Box reports 87% vs 77% internally)
- Turn-by-turn regressions reported: missed obvious instructions in planning docs, answering a narrow slice of the goal, and worse one-shot simple UI generation than 4.7
- Writing style criticized by heavy users: excessive hedging, over-cautious editing that 'cuts anything bold or funny' (Steve Yegge), and pushback loops even against well-evidenced theses
- Language-mixing quirk: users report random Chinese, Cyrillic, or Greek insertions in long research threads
- Visible quality degradation past ~200K tokens in hands-on use despite the advertised 1M window
- Vending-Bench regression: fell for scam suppliers about 30x more than 4.7 and negotiates worse (a side effect of stricter honesty alignment)
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 Claude Opus 4.8
A drop-in upgrade for Opus 4.7 users: identical API surface and $5/$25 pricing with real gains on long-horizon agentic coding, code review, and enterprise analysis. Choose it if you run Claude Code, multi-file migrations, security audits, or agent pipelines that inspect, act, and verify over many steps. Skip it for quick one-shot UI snippets or prompts tightly tuned to 4.7 behavior, where users report regressions, and pick Sonnet 5 ($3/$15, intro $2/$10 through Aug 2026) if cost matters more than ceiling capability. Writers sensitive to hedging and over-cautious editing may find its style frustrating.
What the crowd says
On Mistral Large 3
No verdicts yet. Be the first to speak.
On Claude Opus 4.8
“Writing took a hit. It hedges everything and edits any bold or funny line out of my drafts. Also caught it answering a narrow slice of my planning doc and calling it done.”
“Threw USAMO-level math at it for a lark and it just grinds through. 96.7 vs 69 for 4.7 tracks with what I see. Same $5/$25, 1M context, no excuse not to switch.”
“Upgraded from 4.7 for a monorepo refactor and the difference is real. It actually flags its own sketchy code instead of shipping it. Multi-file bug hunts feel way less babysat.”
Keep comparing
Frequently asked questions
Is Mistral Large 3 better than Claude Opus 4.8?
The crowd currently sides with Claude Opus 4.8: 57% recommend it, versus 50% for Mistral Large 3 (3 votes). On Reasoning, Claude Opus 4.8 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 Claude Opus 4.8?
Mistral Large 3 is cheaper: it starts at $1.50/1M out, while Claude Opus 4.8 starts at $25/1M out.
How much do Mistral Large 3 and Claude Opus 4.8 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 4.8: $5/1M in per 1M input tokens, $25/1M out per 1M output tokens.