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Claude Opus 4.5 vs Mistral Large 2407

Claude Opus 4.5 leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 29.1 to 7.6, and Mistral Large 2407 costs 4.2x less per output token ($6.00 vs $25.00 per million). Updated October 21, 2026; 14 benchmarks have a result for both.

Claude Opus 4.5Anthropic
Coding
44.0
Overall
55.7
Output $/M
$25.00
14
Mistral Large 2407Mistral AI
Coding
No data
Overall
No data
Output $/M
$6.00
0
vs boards won of 14 shared

Claude Opus 4.5 or Mistral Large 2407: which to pick

Only Claude Opus 4.5 has a coding score, 44.0 on the Vibe Coding Index; Mistral Large 2407 has none, so the two cannot be ranked on coding.

Pick Mistral Large 2407 for output tokens 4.2x cheaper ($6.00 vs $25.00 per million).

Shared results
14
Claude Opus 4.5 only
94
Mistral Large 2407 only
3
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Claude Opus 4.5 44.0 from 3 benchmarks, Mistral Large 2407 not benchmarked.
Widest category
Math, 86.8 points apart on average over 3 shared benchmarks.
Widest single gap
AIME 2025 (Artificial Analysis run): Claude Opus 4.5 91.3% against Mistral Large 2407 0.0%, 91.3 points apart on a 0 to 100 scale.
Output price
Claude Opus 4.5 $25.00 against Mistral Large 2407 $6.00 per million output tokens, 4.2x apart.
Context window
Claude Opus 4.5 takes 200K tokens, Mistral Large 2407 131K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryClaude Opus 4.5BarsMistral Large 2407Ties
Coding10
Intelligence10
Agentic work10
Reasoning40
Math30
Knowledge10
General30
All shared boards140
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
Coding benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
SWE-bench Verified79.2%Not measuredOnly Claude Opus 4.5 measured
SWE-bench (full test set)52.6%Not measuredOnly Claude Opus 4.5 measured
SWE-bench Multilingual70.7%Not measuredOnly Claude Opus 4.5 measured
LMArena WebDev1494Not measuredOnly Claude Opus 4.5 measured
LiveCodeBench (Vals AI)83.7%Not measuredOnly Claude Opus 4.5 measured
SWE-bench Verified (Vals AI)76.4%Not measuredOnly Claude Opus 4.5 measured
Vibe Code Bench (Vals AI)20.6%Not measuredOnly Claude Opus 4.5 measured
LiveCodeBench (Artificial Analysis run)87.1%26.7%Claude Opus 4.5
Terminal-Bench Hard (Artificial Analysis run)47.0%Not measuredOnly Claude Opus 4.5 measured
Terminal-Bench63.1%Not measuredOnly Claude Opus 4.5 measured
GSO-Bench26.5%Not measuredOnly Claude Opus 4.5 measured
WeirdML63.7%Not measuredOnly Claude Opus 4.5 measured
Cybench82.0%Not measuredOnly Claude Opus 4.5 measured
LMArena Text: coding1503Not measuredOnly Claude Opus 4.5 measured
LMArena Text: software and IT1483Not measuredOnly Claude Opus 4.5 measured
LMArena WebDev: React1478Not measuredOnly Claude Opus 4.5 measured
LMArena WebDev: HTML1510Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
AA Intelligence Index29.17.6Claude Opus 4.5
Epoch Capabilities Index150.1Not measuredOnly Claude Opus 4.5 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
Design Arena: agents building web apps1159Not measuredOnly Claude Opus 4.5 measured
Design Arena: agents building full-stack apps1156Not measuredOnly Claude Opus 4.5 measured
Design Arena: agents building mobile apps1189Not measuredOnly Claude Opus 4.5 measured
Design Arena: agents building Android apps1175Not measuredOnly Claude Opus 4.5 measured
τ²-Bench Telecom89.5%33.0%Claude Opus 4.5
METR time horizons75.0%Not measuredOnly Claude Opus 4.5 measured
Remote Labor Index3.8%Not measuredOnly Claude Opus 4.5 measured
GDPval45.5%Not measuredOnly Claude Opus 4.5 measured
BALROG43.5%Not measuredOnly Claude Opus 4.5 measured
DeepResearch Bench54.8%Not measuredOnly Claude Opus 4.5 measured
OSWorld66.3%Not measuredOnly Claude Opus 4.5 measured
Vending-Bench 2$4,967Not measuredOnly Claude Opus 4.5 measured
Berkeley Function Calling Leaderboard (BFCL)77.5%Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 28 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
GPQA Diamond85.9%Not measuredOnly Claude Opus 4.5 measured
MMMU Pro (Vals AI)83.0%Not measuredOnly Claude Opus 4.5 measured
GPQA Diamond (Artificial Analysis run)86.6%47.2%Claude Opus 4.5
Humanity's Last Exam (Artificial Analysis run)30.1%2.9%Claude Opus 4.5
GPQA Diamond (Epoch AI run)86.0%49.0%Claude Opus 4.5
ARC-AGI80.0%Not measuredOnly Claude Opus 4.5 measured
ARC-AGI-237.6%Not measuredOnly Claude Opus 4.5 measured
SimpleBench62.0%22.5%Claude Opus 4.5
Chess puzzles14.0%Not measuredOnly Claude Opus 4.5 measured
Mystery game puzzles22.0%Not measuredOnly Claude Opus 4.5 measured
EBR-bench14.3%Not measuredOnly Claude Opus 4.5 measured
VPCT40.0%Not measuredOnly Claude Opus 4.5 measured
LMArena Text: hard prompts1476Not measuredOnly Claude Opus 4.5 measured
LMArena Text: expert1487Not measuredOnly Claude Opus 4.5 measured
LMArena Text: hard prompts in English1482Not measuredOnly Claude Opus 4.5 measured
Kagi LLM Benchmark80.2%Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
AIME95.4%Not measuredOnly Claude Opus 4.5 measured
MGSM (Vals AI)95.2%Not measuredOnly Claude Opus 4.5 measured
AA Math Index91.30.0Claude Opus 4.5
AIME 2025 (Artificial Analysis run)91.3%0.0%Claude Opus 4.5
OTIS Mock AIME86.1%8.5%Claude Opus 4.5
FrontierMath, tiers 1 to 334.4%Not measuredOnly Claude Opus 4.5 measured
FrontierMath, tier 44.9%Not measuredOnly Claude Opus 4.5 measured
ProofBench36.0%Not measuredOnly Claude Opus 4.5 measured
MATH level 5Not measured44.8%Only Mistral Large 2407 measured
LMArena Text: math1463Not measuredOnly Claude Opus 4.5 measured
LMArena Text: mathematical work1468Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
MMLU Pro87.3%Not measuredOnly Claude Opus 4.5 measured
LegalBench (Vals AI)84.6%Not measuredOnly Claude Opus 4.5 measured
CaseLaw (Vals AI)62.6%Not measuredOnly Claude Opus 4.5 measured
CorpFin (Vals AI)65.1%Not measuredOnly Claude Opus 4.5 measured
TaxEval (Vals AI)74.9%Not measuredOnly Claude Opus 4.5 measured
MortgageTax (Vals AI)68.7%Not measuredOnly Claude Opus 4.5 measured
MedQA (Vals AI)95.9%Not measuredOnly Claude Opus 4.5 measured
MedCode (Vals AI)49.2%Not measuredOnly Claude Opus 4.5 measured
MMLU Pro (Artificial Analysis run)89.5%68.3%Claude Opus 4.5
SimpleQA Verified45.7%Not measuredOnly Claude Opus 4.5 measured
MMLUNot measured80.0%Only Mistral Large 2407 measured
GeoBench75.0%Not measuredOnly Claude Opus 4.5 measured
LMArena Text: science1454Not measuredOnly Claude Opus 4.5 measured
LMArena Text: medicine and healthcare1444Not measuredOnly Claude Opus 4.5 measured
LMArena Text: legal and government1459Not measuredOnly Claude Opus 4.5 measured
LMArena Text: business and finance1448Not measuredOnly Claude Opus 4.5 measured
FACTS Benchmark Suite43.9%Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 1 board here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
Design Arena: websites1247Not measuredOnly Claude Opus 4.5 measured
Design Arena: UI components1238Not measuredOnly Claude Opus 4.5 measured
Design Arena: game dev1229Not measuredOnly Claude Opus 4.5 measured
Design Arena: data viz1243Not measuredOnly Claude Opus 4.5 measured
Design Arena: 3D1214Not measuredOnly Claude Opus 4.5 measured
Design Arena: all code categories1242Not measuredOnly Claude Opus 4.5 measured
Design Arena: SVG1181Not measuredOnly Claude Opus 4.5 measured
Design Arena: ASCII art1190Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
General benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
SAGE (Vals AI)52.1%Not measuredOnly Claude Opus 4.5 measured
MedScribe (Vals AI)85.3%Not measuredOnly Claude Opus 4.5 measured
IFBench58.0%31.6%Claude Opus 4.5
Long context reasoning (AA-LCR)77.3%2.0%Claude Opus 4.5
DTBench89.9%61.2%Claude Opus 4.5
LMCA44.5%Not measuredOnly Claude Opus 4.5 measured
Creative writing (Lech Mazur)Not measured69.0%Only Mistral Large 2407 measured
CL-bench21.1%Not measuredOnly Claude Opus 4.5 measured
Furniture assembly28.3%Not measuredOnly Claude Opus 4.5 measured
LMArena Text1451Not measuredOnly Claude Opus 4.5 measured
LMArena Text: instruction following1478Not measuredOnly Claude Opus 4.5 measured
LMArena Text: creative writing1445Not measuredOnly Claude Opus 4.5 measured
LMArena Text: English1461Not measuredOnly Claude Opus 4.5 measured
LMArena Text: non-English1438Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Chinese1470Not measuredOnly Claude Opus 4.5 measured
LMArena Text: German1449Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Japanese1417Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Korean1424Not measuredOnly Claude Opus 4.5 measured
LMArena Text: French1471Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Spanish1457Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Russian1447Not measuredOnly Claude Opus 4.5 measured
LMArena Text: Polish1439Not measuredOnly Claude Opus 4.5 measured
LMArena Text: multi-turn1466Not measuredOnly Claude Opus 4.5 measured
LMArena Text: longer queries1479Not measuredOnly Claude Opus 4.5 measured
LMArena Text: writing and language1452Not measuredOnly Claude Opus 4.5 measured
LMArena Text: entertainment and media1434Not measuredOnly Claude Opus 4.5 measured
LMArena Document1462Not measuredOnly Claude Opus 4.5 measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Claude Opus 4.5 and Mistral Large 2407
BenchmarkClaude Opus 4.5Mistral Large 2407Leader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Claude Opus 4.5$0.018Fits in one request
Mistral Large 2407$0.0050Fits in one request

Mistral Large 2407 costs less

Repository review

50K input + 3K output tokens

Claude Opus 4.5$0.325Fits in one request
Mistral Large 2407$0.118Fits in one request

Mistral Large 2407 costs less

Agent loop

200K input + 10K output tokens

Claude Opus 4.5$1.25Exceeds the context window
Mistral Large 2407$0.460Exceeds the context window

Mistral Large 2407 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Claude Opus 4.5 and Mistral Large 2407
SpecificationAnthropic Claude Opus 4.5Mistral Large 2407
MakerAnthropicMistral AI
WeightsNo public weights listedNo public weights listed
ReleasedNovember 24, 2025November 19, 2024
Context window200K131K
Input $/Mtok$5.00$2.00
Output $/Mtok$25.00$6.00
Coding index44.0Not benchmarked
Independent measurements behind it30
Cost per solved task$1.37Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Claude Opus 4.5 better than Mistral Large 2407 for coding?

Only Claude Opus 4.5 has a coding score, 44.0 on the Vibe Coding Index; Mistral Large 2407 has none, so the two cannot be ranked on coding. Pick Mistral Large 2407 for output tokens 4.2x cheaper ($6.00 vs $25.00 per million).

Which is cheaper, Claude Opus 4.5 or Mistral Large 2407?

Claude Opus 4.5 costs $5.00 per million input tokens and $25.00 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for Claude Opus 4.5 only.

Which has the bigger context window, Claude Opus 4.5 or Mistral Large 2407?

Claude Opus 4.5, with 200K tokens against 131K.

How many benchmarks is the Claude Opus 4.5 vs Mistral Large 2407 comparison based on?

14 benchmarks have a result for both models as of October 21, 2026. Claude Opus 4.5's coding index rests on 3 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.

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