GPT-5.3-Codex vs MiMo-V2.5-Pro
GPT-5.3-Codex leads MiMo-V2.5-Pro on coding, 60.0 to 31.6 on the Vibe Coding Index, and MiMo-V2.5-Pro costs 15.3x less per output token ($0.9152 vs $14.00 per million). Updated October 22, 2026; 22 benchmarks have a result for both.
GPT-5.3-Codex or MiMo-V2.5-Pro: which to pick
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 31.6 for MiMo-V2.5-Pro, though on 3 benchmarks against 4.
Pick MiMo-V2.5-Pro for output tokens 15.3x cheaper ($0.9152 vs $14.00 per million) and a lower cost per solved task ($0.03 against $0.59).
- Shared results
- 22
- GPT-5.3-Codex only
- 12
- MiMo-V2.5-Pro 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
GPT-5.3-Codex
GPT-5.3-Codex scores 60.0 against 31.6.
3 and 4 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 26.0.
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
No call
Only MiMo-V2.5-Pro has a GPQA Diamond result, 82.6%.
Directional only
Knowledge
MMLU Pro
No call
Only MiMo-V2.5-Pro has a MMLU Pro result, 84.6%.
Directional only
Web design
Design Arena: websites
MiMo-V2.5-Pro
MiMo-V2.5-Pro leads, 1266 against 1162.
Same board, both measured
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5-Pro
GPT-5.3-Codex $0.0088, 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-5.3-Codex $0.59, MiMo-V2.5-Pro $0.03.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.3-Codex 60.0 from 3 benchmarks, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Coding, 13.0 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: 3D: GPT-5.3-Codex 1007 against MiMo-V2.5-Pro 1243, 29.5 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against MiMo-V2.5-Pro $0.9152 per million output tokens, 15.3x apart.
- Context window
- GPT-5.3-Codex takes 400K 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-5.3-Codex | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 5 | 3 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| Web design | 0 | 8 | ||
| General | 1 | 1 | ||
| All shared boards | 9 | 13 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index53
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | 1480 | MiMo-V2.5-Pro |
| AA Coding Index | Not measured | 60.2 | Only MiMo-V2.5-Pro measured |
| LiveCodeBench (Vals AI) | 87.3% | 81.3% | GPT-5.3-Codex |
| SWE-bench Verified (Vals AI) | 78.0% | 74.0% | GPT-5.3-Codex |
| Terminal-Bench 4 (Vals AI) | Not measured | 0.5% | Only MiMo-V2.5-Pro measured |
| Vibe Code Bench (Vals AI) | 61.8% | 34.1% | GPT-5.3-Codex |
| Code migration (Vals AI) | Not measured | 21.6% | Only MiMo-V2.5-Pro measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 57.3% | Only MiMo-V2.5-Pro measured |
| SciCode | Not measured | 50.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 43.2% | GPT-5.3-Codex |
| Terminal-Bench | 78.4% | Not measured | Only GPT-5.3-Codex measured |
| WeirdML | 79.3% | Not measured | Only GPT-5.3-Codex measured |
| ALE-Bench | 1655.2 | 899.8 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1501 | Only MiMo-V2.5-Pro measured |
| LMArena Text: software and IT | Not measured | 1495 | Only MiMo-V2.5-Pro measured |
| LMArena WebDev: React | 1395 | 1469 | MiMo-V2.5-Pro |
| LMArena WebDev: HTML | 1422 | 1473 | MiMo-V2.5-Pro |
| LMArena WebDev: from an image | 1443 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 26.0 | GPT-5.3-Codex |
| Epoch Capabilities Index | 157.1 | Not measured | Only GPT-5.3-Codex measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 21.3 | Only MiMo-V2.5-Pro measured |
| Design Arena: agents building web apps | 1032 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building full-stack apps | 983 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building mobile apps | 1053 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Android apps | 1084 | Not measured | Only GPT-5.3-Codex measured |
| Design Arena: agents building Godot games | 1124 | Not measured | Only GPT-5.3-Codex 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 | 86.0% | 94.2% | MiMo-V2.5-Pro |
| METR time horizons | 74.5% | Not measured | Only GPT-5.3-Codex measured |
| Vending-Bench 2 | $5,940 | Not measured | Only GPT-5.3-Codex measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 82.6% | Only MiMo-V2.5-Pro measured |
| GPQA Diamond (Artificial Analysis run) | 91.5% | 86.6% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 35.7% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1486 | Only MiMo-V2.5-Pro measured |
| LMArena Text: expert | Not measured | 1501 | Only MiMo-V2.5-Pro measured |
| LMArena Text: hard prompts in English | Not measured | 1494 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| ProofBench | Not measured | 22.0% | Only MiMo-V2.5-Pro measured |
| LMArena Text: math | Not measured | 1481 | Only MiMo-V2.5-Pro measured |
| LMArena Text: mathematical work | Not measured | 1491 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.6% | Only MiMo-V2.5-Pro measured |
| LegalBench (Vals AI) | Not measured | 77.1% | Only MiMo-V2.5-Pro measured |
| CorpFin (Vals AI) | Not measured | 61.4% | Only MiMo-V2.5-Pro measured |
| TaxEval (Vals AI) | Not measured | 73.8% | Only MiMo-V2.5-Pro measured |
| MedCode (Vals AI) | Not measured | 32.5% | Only MiMo-V2.5-Pro measured |
| LMArena Text: science | Not measured | 1482 | Only MiMo-V2.5-Pro measured |
| LMArena Text: medicine and healthcare | Not measured | 1473 | Only MiMo-V2.5-Pro measured |
| LMArena Text: legal and government | Not measured | 1474 | Only MiMo-V2.5-Pro measured |
| LMArena Text: business and finance | Not measured | 1466 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | 1162 | 1266 | MiMo-V2.5-Pro |
| Design Arena: UI components | 1134 | 1246 | MiMo-V2.5-Pro |
| Design Arena: game dev | 1161 | 1261 | MiMo-V2.5-Pro |
| Design Arena: data viz | 1164 | 1257 | MiMo-V2.5-Pro |
| Design Arena: 3D | 1007 | 1243 | MiMo-V2.5-Pro |
| Design Arena: all code categories | 1144 | 1265 | MiMo-V2.5-Pro |
| Design Arena: SVG | 1129 | 1181 | MiMo-V2.5-Pro |
| Design Arena: ASCII art | 1152 | 1156 | MiMo-V2.5-Pro |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GPT-5.3-Codex | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.7% | Only MiMo-V2.5-Pro measured |
| IFBench | 75.4% | 79.9% | MiMo-V2.5-Pro |
| Long context reasoning (AA-LCR) | 83.3% | 79.7% | GPT-5.3-Codex |
| DTBench | Not measured | 84.5% | Only MiMo-V2.5-Pro measured |
| LMCA | Not measured | 29.5% | Only MiMo-V2.5-Pro measured |
| LMArena Text | Not measured | 1464 | Only MiMo-V2.5-Pro measured |
| LMArena Text: instruction following | Not measured | 1475 | Only MiMo-V2.5-Pro measured |
| LMArena Text: creative writing | Not measured | 1438 | Only MiMo-V2.5-Pro measured |
| LMArena Text: English | Not measured | 1477 | Only MiMo-V2.5-Pro measured |
| LMArena Text: non-English | Not measured | 1448 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Chinese | Not measured | 1507 | Only MiMo-V2.5-Pro measured |
| LMArena Text: German | Not measured | 1459 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Japanese | Not measured | 1410 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Korean | Not measured | 1435 | Only MiMo-V2.5-Pro measured |
| LMArena Text: French | Not measured | 1487 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Spanish | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Russian | Not measured | 1449 | Only MiMo-V2.5-Pro measured |
| LMArena Text: Polish | Not measured | 1464 | Only MiMo-V2.5-Pro measured |
| LMArena Text: multi-turn | Not measured | 1476 | Only MiMo-V2.5-Pro measured |
| LMArena Text: longer queries | Not measured | 1481 | Only MiMo-V2.5-Pro measured |
| LMArena Text: writing and language | Not measured | 1451 | Only MiMo-V2.5-Pro measured |
| LMArena Text: entertainment and media | Not measured | 1438 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 24 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.3-Codex | 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-5.3-Codex | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | OpenAI | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | February 24, 2026 | April 22, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.75 | $0.4575 |
| Output $/Mtok | $14.00 | $0.9152 |
| Coding index | 60.0 | 31.6 |
| Independent measurements behind it | 3 | 4 |
| Cost per solved task | $0.59 | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than MiMo-V2.5-Pro for coding?
Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 31.6 for MiMo-V2.5-Pro, though on 3 benchmarks against 4. Pick MiMo-V2.5-Pro for output tokens 15.3x cheaper ($0.9152 vs $14.00 per million) and a lower cost per solved task ($0.03 against $0.59).
Which is cheaper, GPT-5.3-Codex or MiMo-V2.5-Pro?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and MiMo-V2.5-Pro $0.03.
Which has the bigger context window, GPT-5.3-Codex or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs MiMo-V2.5-Pro comparison based on?
22 benchmarks have a result for both models as of October 22, 2026. GPT-5.3-Codex's coding index rests on 3 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.