Mistral Large 2407 vs Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads Mistral Large 2407 on the Artificial Analysis Intelligence Index, 7.7 to 7.6, and Qwen2.5 72B Instruct costs 15.0x less per output token ($0.40 vs $6.00 per million). Updated October 21, 2026; 14 benchmarks have a result for both.
Mistral Large 2407 or Qwen2.5 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Mistral Large 2407 for a larger context window (131K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
- Shared results
- 14
- Mistral Large 2407 only
- 3
- Qwen2.5 72B Instruct only
- 39
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct leads, 7.7 against 7.6.
Same board, both measured
Long documents
Listed context window
Mistral Large 2407
Mistral Large 2407 takes 131K tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
Mistral Large 2407 $0.0050, Qwen2.5 72B Instruct $0.0006 per turn.
List prices
At a glance
- Coding index
- Mistral Large 2407 not benchmarked, Qwen2.5 72B Instruct not benchmarked.
- Widest category
- Math, 11.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- MATH level 5: Mistral Large 2407 44.8% against Qwen2.5 72B Instruct 63.2%, 18.4 points apart on a 0 to 100 scale.
- Output price
- Mistral Large 2407 $6.00 against Qwen2.5 72B Instruct $0.40 per million output tokens, 15.0x apart.
- Context window
- Mistral Large 2407 takes 131K tokens, Qwen2.5 72B Instruct 33K.
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.
| Category | Mistral Large 2407 | Bars | Qwen2.5 72B Instruct | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 1 | 3 | ||
| Knowledge | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| LiveCodeBench (Artificial Analysis run) | 26.7% | 27.6% | Qwen2.5 72B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 4.5% | Only Qwen2.5 72B Instruct measured |
| WeirdML | Not measured | 16.0% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: coding | Not measured | 1292 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: software and IT | Not measured | 1292 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.6 | 7.7 | Qwen2.5 72B Instruct |
| Epoch Capabilities Index | Not measured | 129.0 | Only Qwen2.5 72B Instruct measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 33.0% | 34.5% | Qwen2.5 72B Instruct |
| METR time horizons | Not measured | 35.8% | Only Qwen2.5 72B Instruct measured |
| BALROG | Not measured | 16.2% | Only Qwen2.5 72B Instruct measured |
| TheAgentCompany | Not measured | 5.7% | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 47.2% | 49.1% | Qwen2.5 72B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 2.9% | 3.6% | Qwen2.5 72B Instruct |
| GPQA Diamond (Epoch AI run) | 49.0% | 49.1% | Qwen2.5 72B Instruct |
| SimpleBench | 22.5% | Not measured | Only Mistral Large 2407 measured |
| BIG-Bench Hard | Not measured | 79.8% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts | Not measured | 1271 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: expert | Not measured | 1245 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts in English | Not measured | 1279 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 14 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| AA Math Index | 0.0 | 14.0 | Qwen2.5 72B Instruct |
| AIME 2025 (Artificial Analysis run) | 0.0% | 14.0% | Qwen2.5 72B Instruct |
| OTIS Mock AIME | 8.5% | 8.1% | Mistral Large 2407 |
| MATH level 5 | 44.8% | 63.2% | Qwen2.5 72B Instruct |
| LMArena Text: math | Not measured | 1283 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: mathematical work | Not measured | 1290 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 68.3% | 72.0% | Qwen2.5 72B Instruct |
| MMLU | 80.0% | 85.3% | Qwen2.5 72B Instruct |
| AI2 Reasoning Challenge | Not measured | 94.5% | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | Not measured | 1275 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: medicine and healthcare | Not measured | 1258 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: legal and government | Not measured | 1288 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: business and finance | Not measured | 1253 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 11 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| IFBench | 31.6% | 36.9% | Qwen2.5 72B Instruct |
| Long context reasoning (AA-LCR) | 2.0% | Not measured | Only Mistral Large 2407 measured |
| DTBench | 61.2% | 62.9% | Qwen2.5 72B Instruct |
| LMCA | Not measured | 13.4% | Only Qwen2.5 72B Instruct measured |
| Creative writing (Lech Mazur) | 69.0% | Not measured | Only Mistral Large 2407 measured |
| HellaSwag | Not measured | 84.8% | Only Qwen2.5 72B Instruct measured |
| WinoGrande | Not measured | 82.3% | Only Qwen2.5 72B Instruct measured |
| PIQA | Not measured | 82.6% | Only Qwen2.5 72B Instruct measured |
| LMArena Text | Not measured | 1269 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: instruction following | Not measured | 1254 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: creative writing | Not measured | 1221 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: English | Not measured | 1283 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: non-English | Not measured | 1252 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Chinese | Not measured | 1273 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: German | Not measured | 1234 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Japanese | Not measured | 1180 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Korean | Not measured | 1188 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: French | Not measured | 1280 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Spanish | Not measured | 1255 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Russian | Not measured | 1264 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: multi-turn | Not measured | 1272 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: longer queries | Not measured | 1281 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: writing and language | Not measured | 1247 | Only Qwen2.5 72B Instruct measured |
| LMArena Text: entertainment and media | Not measured | 1211 | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Mistral Large 2407 | Qwen2.5 72B Instruct | Leader |
|---|---|---|---|
| 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
Qwen2.5 72B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 72B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 72B Instruct 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.
| Specification | Mistral Large 2407 | Qwen2.5 72B Instruct |
|---|---|---|
| Maker | Mistral AI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | November 19, 2024 | September 19, 2024 |
| Context window | 131K | 33K |
| Input $/Mtok | $2.00 | $0.36 |
| Output $/Mtok | $6.00 | $0.40 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Mistral Large 2407 better than Qwen2.5 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Mistral Large 2407 for a larger context window (131K against 33K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or AA Intelligence Index, where it leads.
Which is cheaper, Mistral Large 2407 or Qwen2.5 72B Instruct?
Mistral Large 2407 costs $2.00 per million input tokens and $6.00 per million output tokens; Qwen2.5 72B Instruct costs $0.36 and $0.40. Cost per solved task is known for neither of them.
Which has the bigger context window, Mistral Large 2407 or Qwen2.5 72B Instruct?
Mistral Large 2407, with 131K tokens against 33K.
How many benchmarks is the Mistral Large 2407 vs Qwen2.5 72B Instruct comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. Mistral Large 2407's coding index rests on 0 independent benchmarks and Qwen2.5 72B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.