GPT-4.1 vs MiMo-V2.5-Pro
MiMo-V2.5-Pro leads GPT-4.1 on the Artificial Analysis Intelligence Index, 26.0 to 12.7, and MiMo-V2.5-Pro costs 8.7x less per output token ($0.9152 vs $8.00 per million). Updated October 22, 2026; 50 benchmarks have a result for both.
GPT-4.1 or MiMo-V2.5-Pro: which to pick
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding.
Pick GPT-4.1 for work measured by LegalBench (Vals AI) or CorpFin (Vals AI), where it leads.
- Shared results
- 50
- GPT-4.1 only
- 42
- MiMo-V2.5-Pro only
- 20
- Like-for-like categories
- 8 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 MiMo-V2.5-Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 26.0 against 12.7.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiMo-V2.5-Pro has a AA Agentic Index result, 21.3.
Directional only
Reasoning
GPQA Diamond
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 82.6% against 65.4%.
Same board, both measured
Math
AIME
No call
Only GPT-4.1 has a AIME result, 39.6%.
Directional only
Knowledge
MMLU Pro
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 84.6% against 80.5%.
Same board, both measured
Web design
Design Arena: websites
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 1266 against 1038.
Same board, both measured
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5-Pro
GPT-4.1 $0.0060, MiMo-V2.5-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiMo-V2.5-Pro
GPT-4.1 $0.37, MiMo-V2.5-Pro $0.03.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-4.1 not benchmarked, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Agentic work, 47.1 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: GPT-4.1 849 against MiMo-V2.5-Pro 1243, 49.3 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 $8.00 against MiMo-V2.5-Pro $0.9152 per million output tokens, 8.7x apart.
- Context window
- GPT-4.1 takes 1.05M tokens, MiMo-V2.5-Pro 1.05M.
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 | GPT-4.1 | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 3 | 5 | ||
| Web design | 0 | 6 | ||
| General | 0 | 21 | ||
| All shared boards | 3 | 47 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Aider polyglot | 52.4% | Not measured | Only GPT-4.1 measured |
| SWE-bench Verified | 39.6% | Not measured | Only GPT-4.1 measured |
| SWE-bench Multimodal | 31.1% | Not measured | Only GPT-4.1 measured |
| LMArena WebDev | Not measured | 1480 | Only MiMo-V2.5-Pro measured |
| AA Coding Index | Not measured | 60.2 | Only MiMo-V2.5-Pro measured |
| LiveCodeBench (Vals AI) | 54.7% | 81.3% | MiMo-V2.5-Pro |
| SWE-bench Verified (Vals AI) | Not measured | 74.0% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only MiMo-V2.5-Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 34.1% | Only MiMo-V2.5-Pro measured |
| Code migration (Vals AI) | Not measured | 21.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 57.3% | Only MiMo-V2.5-Pro measured |
| LiveCodeBench (Artificial Analysis run) | 45.7% | Not measured | Only GPT-4.1 measured |
| SciCode | Not measured | 50.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | 43.2% | MiMo-V2.5-Pro |
| WeirdML | 39.0% | Not measured | Only GPT-4.1 measured |
| ALE-Bench | 558.1 | 899.8 | MiMo-V2.5-Pro |
| LMArena Text: coding | 1390 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: software and IT | 1391 | 1495 | MiMo-V2.5-Pro |
| LMArena WebDev: React | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena WebDev: HTML | Not measured | 1473 | Only MiMo-V2.5-Pro measured |
| SciCode | 4.6% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | 26.0 | MiMo-V2.5-Pro |
| Epoch Capabilities Index | 136.7 | Not measured | Only GPT-4.1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 21.3 | Only MiMo-V2.5-Pro measured |
| Finance Agent (Vals AI) | Not measured | 41.5% | Only MiMo-V2.5-Pro measured |
| Tax Agent Bench (Vals AI) | Not measured | 11.5% | Only MiMo-V2.5-Pro measured |
| Legal Research Bench (Vals AI) | Not measured | 15.9% | Only MiMo-V2.5-Pro measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.2% | Only MiMo-V2.5-Pro measured |
| τ²-Bench Telecom | 47.1% | 94.2% | MiMo-V2.5-Pro |
| Berkeley Function Calling Leaderboard (BFCL) | 54.0% | Not measured | Only GPT-4.1 measured |
| BrowseComp-Plus | 36.4% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 65.4% | 82.6% | MiMo-V2.5-Pro |
| MMMU Pro (Vals AI) | 72.4% | Not measured | Only GPT-4.1 measured |
| GPQA Diamond (Artificial Analysis run) | 66.6% | 86.6% | MiMo-V2.5-Pro |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 35.7% | MiMo-V2.5-Pro |
| GPQA Diamond (Epoch AI run) | 66.9% | Not measured | Only GPT-4.1 measured |
| ARC-AGI | 5.5% | Not measured | Only GPT-4.1 measured |
| ARC-AGI-2 | 0.4% | Not measured | Only GPT-4.1 measured |
| SimpleBench | 27.0% | Not measured | Only GPT-4.1 measured |
| Chess puzzles | 3.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: hard prompts | 1384 | 1486 | MiMo-V2.5-Pro |
| LMArena Text: expert | 1364 | 1501 | MiMo-V2.5-Pro |
| LMArena Text: hard prompts in English | 1393 | 1494 | MiMo-V2.5-Pro |
| LMArena Vision: diagrams | 1215 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: homework | 1248 | Not measured | Only GPT-4.1 measured |
| Kagi LLM Benchmark | 52.3% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AIME | 39.6% | Not measured | Only GPT-4.1 measured |
| MATH 500 (Vals AI) | 87.2% | Not measured | Only GPT-4.1 measured |
| MGSM (Vals AI) | 87.7% | Not measured | Only GPT-4.1 measured |
| AA Math Index | 34.7 | Not measured | Only GPT-4.1 measured |
| AIME 2025 (Artificial Analysis run) | 34.7% | Not measured | Only GPT-4.1 measured |
| OTIS Mock AIME | 38.3% | Not measured | Only GPT-4.1 measured |
| FrontierMath, tiers 1 to 3 | 6.0% | Not measured | Only GPT-4.1 measured |
| ProofBench | Not measured | 22.0% | Only MiMo-V2.5-Pro measured |
| MATH level 5 | 83.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: math | 1369 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: mathematical work | 1360 | 1491 | MiMo-V2.5-Pro |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index35
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | 80.5% | 84.6% | MiMo-V2.5-Pro |
| LegalBench (Vals AI) | 83.1% | 77.1% | GPT-4.1 |
| CaseLaw (Vals AI) | 69.9% | Not measured | Only GPT-4.1 measured |
| CorpFin (Vals AI) | 63.0% | 61.4% | GPT-4.1 |
| TaxEval (Vals AI) | 75.1% | 73.8% | GPT-4.1 |
| MortgageTax (Vals AI) | 65.9% | Not measured | Only GPT-4.1 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only GPT-4.1 measured |
| MedCode (Vals AI) | Not measured | 32.5% | Only MiMo-V2.5-Pro measured |
| MMLU Pro (Artificial Analysis run) | 80.6% | Not measured | Only GPT-4.1 measured |
| SimpleQA Verified | 31.1% | Not measured | Only GPT-4.1 measured |
| GeoBench | 72.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: science | 1382 | 1482 | MiMo-V2.5-Pro |
| LMArena Text: medicine and healthcare | 1379 | 1473 | MiMo-V2.5-Pro |
| LMArena Text: legal and government | 1399 | 1474 | MiMo-V2.5-Pro |
| LMArena Text: business and finance | 1369 | 1466 | MiMo-V2.5-Pro |
| FACTS Benchmark Suite | 45.8% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index06
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1038 | 1266 | MiMo-V2.5-Pro |
| Design Arena: UI components | 997 | 1246 | MiMo-V2.5-Pro |
| Design Arena: game dev | 1079 | 1261 | MiMo-V2.5-Pro |
| Design Arena: data viz | 1104 | 1257 | MiMo-V2.5-Pro |
| Design Arena: 3D | 849 | 1243 | MiMo-V2.5-Pro |
| Design Arena: all code categories | 1025 | 1265 | MiMo-V2.5-Pro |
| Design Arena: SVG | Not measured | 1181 | Only MiMo-V2.5-Pro measured |
| Design Arena: ASCII art | Not measured | 1156 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.7% | Only MiMo-V2.5-Pro measured |
| IFBench | 43.0% | 79.9% | MiMo-V2.5-Pro |
| Long context reasoning (AA-LCR) | 68.3% | 79.7% | MiMo-V2.5-Pro |
| DTBench | 68.3% | 84.5% | MiMo-V2.5-Pro |
| LMCA | 25.6% | 29.5% | MiMo-V2.5-Pro |
| Fiction.LiveBench | 63.9% | Not measured | Only GPT-4.1 measured |
| LMArena Text | 1383 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: instruction following | 1367 | 1475 | MiMo-V2.5-Pro |
| LMArena Text: creative writing | 1363 | 1438 | MiMo-V2.5-Pro |
| LMArena Vision | 1210 | Not measured | Only GPT-4.1 measured |
| LMArena Text: English | 1392 | 1477 | MiMo-V2.5-Pro |
| LMArena Text: non-English | 1370 | 1448 | MiMo-V2.5-Pro |
| LMArena Text: Chinese | 1382 | 1507 | MiMo-V2.5-Pro |
| LMArena Text: German | 1381 | 1459 | MiMo-V2.5-Pro |
| LMArena Text: Japanese | 1319 | 1410 | MiMo-V2.5-Pro |
| LMArena Text: Korean | 1338 | 1435 | MiMo-V2.5-Pro |
| LMArena Text: French | 1382 | 1487 | MiMo-V2.5-Pro |
| LMArena Text: Spanish | 1376 | 1469 | MiMo-V2.5-Pro |
| LMArena Text: Russian | 1377 | 1449 | MiMo-V2.5-Pro |
| LMArena Text: Polish | 1389 | 1464 | MiMo-V2.5-Pro |
| LMArena Text: multi-turn | 1397 | 1476 | MiMo-V2.5-Pro |
| LMArena Text: longer queries | 1385 | 1481 | MiMo-V2.5-Pro |
| LMArena Text: writing and language | 1371 | 1451 | MiMo-V2.5-Pro |
| LMArena Text: entertainment and media | 1365 | 1438 | MiMo-V2.5-Pro |
| LMArena Vision: English | 1209 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: Chinese | 1226 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: reading text | 1218 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: humor | 1196 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: captioning | 1207 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: recognizing things | 1208 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 | MiMo-V2.5-Pro | 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
MiMo-V2.5-Pro costs less
Repository review
50K input + 3K output tokens
MiMo-V2.5-Pro costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.5-Pro 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 | OpenAI GPT-4.1 | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | OpenAI | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | April 22, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $2.00 | $0.4575 |
| Output $/Mtok | $8.00 | $0.9152 |
| Coding index | Not benchmarked | 31.6 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | $0.37 | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 better than MiMo-V2.5-Pro for coding?
Only MiMo-V2.5-Pro has a coding score, 31.6 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding. Pick GPT-4.1 for work measured by LegalBench (Vals AI) or CorpFin (Vals AI), where it leads.
Which is cheaper, GPT-4.1 or MiMo-V2.5-Pro?
GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Per solved SWE-bench Verified task, GPT-4.1 costs $0.37 and MiMo-V2.5-Pro $0.03.
Which has the bigger context window, GPT-4.1 or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-4.1 vs MiMo-V2.5-Pro comparison based on?
50 benchmarks have a result for both models as of October 22, 2026. GPT-4.1's coding index rests on 0 independent benchmarks and MiMo-V2.5-Pro's on 4. Where only one model has a result, the page says so instead of naming a winner.