Qwen2.5 72B Instruct vs Qwen3.7 Max
Qwen3.7 Max leads Qwen2.5 72B Instruct on the Artificial Analysis Intelligence Index, 29.5 to 7.7, and Qwen2.5 72B Instruct costs 11.1x less per output token ($0.40 vs $4.425 per million). Updated October 22, 2026; 11 benchmarks have a result for both.
Qwen2.5 72B Instruct or Qwen3.7 Max: which to pick
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding.
Pick Qwen2.5 72B Instruct for output tokens 11.1x cheaper ($0.40 vs $4.425 per million).
- Shared results
- 11
- Qwen2.5 72B Instruct only
- 42
- Qwen3.7 Max only
- 48
- Like-for-like categories
- 6 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
Only Qwen3.7 Max has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 7.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.7 Max has a GPQA Diamond result, 90.2%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.7 Max has a MMLU Pro result, 89.3%.
Directional only
Web design
Design Arena: websites
No call
Only Qwen3.7 Max has a Design Arena: websites result, 1274.
Directional only
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 72B Instruct
Qwen2.5 72B Instruct $0.0006, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.7 Max has the number, $4.69.
Directional only
At a glance
- Coding index
- Qwen2.5 72B Instruct not benchmarked, Qwen3.7 Max 54.1 from 3 benchmarks.
- Widest category
- Math, 87.5 points apart on average over 1 shared benchmark.
- Widest single gap
- OTIS Mock AIME: Qwen2.5 72B Instruct 8.1% against Qwen3.7 Max 95.6%, 87.5 points apart on a 0 to 100 scale.
- Output price
- Qwen2.5 72B Instruct $0.40 against Qwen3.7 Max $4.425 per million output tokens, 11.1x apart.
- Context window
- Qwen2.5 72B Instruct takes 33K tokens, Qwen3.7 Max 1M.
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 | Qwen2.5 72B Instruct | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 3 | ||
| Math | 0 | 1 | ||
| General | 0 | 3 | ||
| All shared boards | 0 | 11 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1515 | Only Qwen3.7 Max measured |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | Not measured | 87.1% | Only Qwen3.7 Max measured |
| SWE-bench Verified (Vals AI) | Not measured | 68.8% | Only Qwen3.7 Max measured |
| Vibe Code Bench (Vals AI) | Not measured | 47.7% | Only Qwen3.7 Max measured |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| LiveCodeBench (Artificial Analysis run) | 27.6% | Not measured | Only Qwen2.5 72B Instruct measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 4.5% | 50.8% | Qwen3.7 Max |
| WeirdML | 16.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| ALE-Bench | Not measured | 1189.4 | Only Qwen3.7 Max measured |
| LMArena Text: coding | 1292 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: software and IT | 1292 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena WebDev: React | Not measured | 1514 | Only Qwen3.7 Max measured |
| LMArena WebDev: HTML | Not measured | 1512 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 7.7 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 129.0 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | Not measured | 1191 | Only Qwen3.7 Max measured |
| Design Arena: agents building mobile apps | Not measured | 1155 | Only Qwen3.7 Max measured |
| Design Arena: agents building Android apps | Not measured | 1159 | Only Qwen3.7 Max measured |
| Design Arena: agents building Godot games | Not measured | 1187 | Only Qwen3.7 Max measured |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | Not measured | 1108 | Only Qwen3.7 Max measured |
| Design Arena: PowerPoint slides | Not measured | 1171 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 34.5% | 94.7% | Qwen3.7 Max |
| METR time horizons | 35.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| BALROG | 16.2% | Not measured | Only Qwen2.5 72B Instruct measured |
| TheAgentCompany | 5.7% | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 90.2% | Only Qwen3.7 Max measured |
| GPQA Diamond (Artificial Analysis run) | 49.1% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 3.6% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 49.1% | 90.9% | Qwen3.7 Max |
| SimpleBench | Not measured | 70.4% | Only Qwen3.7 Max measured |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| BIG-Bench Hard | 79.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts | 1271 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: expert | 1245 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: hard prompts in English | 1279 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Math Index | 14.0 | Not measured | Only Qwen2.5 72B Instruct measured |
| AIME 2025 (Artificial Analysis run) | 14.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| OTIS Mock AIME | 8.1% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| MATH level 5 | 63.2% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: math | 1283 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: mathematical work | 1290 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 89.3% | Only Qwen3.7 Max measured |
| LegalBench (Vals AI) | Not measured | 84.9% | Only Qwen3.7 Max measured |
| CorpFin (Vals AI) | Not measured | 63.7% | Only Qwen3.7 Max measured |
| TaxEval (Vals AI) | Not measured | 75.3% | Only Qwen3.7 Max measured |
| MedCode (Vals AI) | Not measured | 38.8% | Only Qwen3.7 Max measured |
| MMLU Pro (Artificial Analysis run) | 72.0% | Not measured | Only Qwen2.5 72B Instruct measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| MMLU | 85.3% | Not measured | Only Qwen2.5 72B Instruct measured |
| AI2 Reasoning Challenge | 94.5% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: science | 1275 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: medicine and healthcare | 1258 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: legal and government | 1288 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: business and finance | 1253 | Not measured | Only Qwen2.5 72B Instruct measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1274 | Only Qwen3.7 Max measured |
| Design Arena: UI components | Not measured | 1267 | Only Qwen3.7 Max measured |
| Design Arena: game dev | Not measured | 1264 | Only Qwen3.7 Max measured |
| Design Arena: data viz | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: 3D | Not measured | 1265 | Only Qwen3.7 Max measured |
| Design Arena: all code categories | Not measured | 1275 | Only Qwen3.7 Max measured |
| Design Arena: SVG | Not measured | 1216 | Only Qwen3.7 Max measured |
| Design Arena: ASCII art | Not measured | 1211 | Only Qwen3.7 Max measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | Qwen2.5 72B Instruct | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.4% | Only Qwen3.7 Max measured |
| IFBench | 36.9% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | Not measured | 79.0% | Only Qwen3.7 Max measured |
| DTBench | 62.9% | 92.3% | Qwen3.7 Max |
| LMCA | 13.4% | 44.0% | Qwen3.7 Max |
| HellaSwag | 84.8% | Not measured | Only Qwen2.5 72B Instruct measured |
| WinoGrande | 82.3% | Not measured | Only Qwen2.5 72B Instruct measured |
| PIQA | 82.6% | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text | 1269 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: instruction following | 1254 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: creative writing | 1221 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: English | 1283 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: non-English | 1252 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Chinese | 1273 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: German | 1234 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Japanese | 1180 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Korean | 1188 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: French | 1280 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Spanish | 1255 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: Russian | 1264 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: multi-turn | 1272 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: longer queries | 1281 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: writing and language | 1247 | Not measured | Only Qwen2.5 72B Instruct measured |
| LMArena Text: entertainment and media | 1211 | Not measured | 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 | Qwen2.5 72B Instruct | Qwen3.7 Max | 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 | Qwen2.5 72B Instruct | Qwen3.7 Max |
|---|---|---|
| Maker | Qwen | Qwen |
| Weights | Open weights | No public weights listed |
| Released | September 19, 2024 | May 21, 2026 |
| Context window | 33K | 1M |
| Input $/Mtok | $0.36 | $1.475 |
| Output $/Mtok | $0.40 | $4.425 |
| Coding index | Not benchmarked | 54.1 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Qwen2.5 72B Instruct better than Qwen3.7 Max for coding?
Only Qwen3.7 Max has a coding score, 54.1 on the Vibe Coding Index; Qwen2.5 72B Instruct has none, so the two cannot be ranked on coding. Pick Qwen2.5 72B Instruct for output tokens 11.1x cheaper ($0.40 vs $4.425 per million).
Which is cheaper, Qwen2.5 72B Instruct or Qwen3.7 Max?
Qwen2.5 72B Instruct costs $0.36 per million input tokens and $0.40 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Cost per solved task is known for Qwen3.7 Max only.
Which has the bigger context window, Qwen2.5 72B Instruct or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 33K.
How many benchmarks is the Qwen2.5 72B Instruct vs Qwen3.7 Max comparison based on?
11 benchmarks have a result for both models as of October 22, 2026. Qwen2.5 72B Instruct's coding index rests on 0 independent benchmarks and Qwen3.7 Max's on 3. Where only one model has a result, the page says so instead of naming a winner.