GPT-5.3-Codex vs MiMo-V2.5
Updated October 21, 2026. 22 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick MiMo-V2.5 for coding: it scores 34.0 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex.
Pick GPT-5.3-Codex when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
- Shared results
- 22
- GPT-5.3-Codex only
- 12
- MiMo-V2.5 only
- 58
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
MiMo-V2.5
MiMo-V2.5 scores 34.0 against 29.5.
One benchmark behind each score
Intelligence
AA Intelligence Index
GPT-5.3-Codex
GPT-5.3-Codex leads, 32.5 against 21.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only MiMo-V2.5 has a AA Agentic Index result, 15.8.
Directional only
Reasoning
GPQA Diamond
No call
Only MiMo-V2.5 has a GPQA Diamond result, 81.6%.
Directional only
Knowledge
MMLU Pro
No call
Only MiMo-V2.5 has a MMLU Pro result, 82.9%.
Directional only
Web design
Design Arena: websites
MiMo-V2.5
MiMo-V2.5 leads, 1266 against 1163.
Same board, both measured
Long documents
Listed context window
MiMo-V2.5
MiMo-V2.5 takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5
GPT-5.3-Codex $0.0088, MiMo-V2.5 $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
MiMo-V2.5
GPT-5.3-Codex $0.59, MiMo-V2.5 $0.01.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-5.3-Codex 29.5 from 1 benchmark, MiMo-V2.5 34.0 from 1 benchmark.
- Widest category
- Web design, 12.1 points apart on average over 8 shared benchmarks.
- Widest single gap
- ALE-Bench: GPT-5.3-Codex 1655.2 against MiMo-V2.5 514.0, 38.0 points apart on a 0 to 100 scale.
- Output price
- GPT-5.3-Codex $14.00 against MiMo-V2.5 $0.238 per million output tokens, 58.8x apart.
- Context window
- GPT-5.3-Codex takes 400K tokens, MiMo-V2.5 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 | Ties |
|---|---|---|---|---|
| Coding | 5 | 3 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 0 | ||
| Web design | 1 | 7 | ||
| General | 2 | 0 | ||
| All shared boards | 11 | 11 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index53
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| LMArena WebDev | 1407 | 1438 | MiMo-V2.5 |
| AA Coding Index | Not measured | 56.8 | Only MiMo-V2.5 measured |
| LiveCodeBench (Vals AI) | 87.3% | 81.5% | GPT-5.3-Codex |
| SWE-bench Verified (Vals AI) | 78.0% | 71.0% | GPT-5.3-Codex |
| Vibe Code Bench (Vals AI) | 61.8% | 42.2% | GPT-5.3-Codex |
| Code migration (Vals AI) | Not measured | 14.2% | Only MiMo-V2.5 measured |
| IOI (Vals AI) | 53.8% | Not measured | Only GPT-5.3-Codex measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 60.7% | Only MiMo-V2.5 measured |
| SciCode | Not measured | 43.9% | Only MiMo-V2.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 53.0% | 41.7% | 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 | 514.0 | GPT-5.3-Codex |
| LMArena Text: coding | Not measured | 1469 | Only MiMo-V2.5 measured |
| LMArena Text: software and IT | Not measured | 1463 | Only MiMo-V2.5 measured |
| LMArena WebDev: React | 1395 | 1425 | MiMo-V2.5 |
| LMArena WebDev: HTML | 1422 | 1429 | MiMo-V2.5 |
| 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 | Leader |
|---|---|---|---|
| AA Intelligence Index | 32.5 | 21.8 | 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 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 15.8 | Only MiMo-V2.5 measured |
| Design Arena: agents building web apps | 1033 | 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 | 1054 | 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 | 36.7% | Only MiMo-V2.5 measured |
| Tax Agent Bench (Vals AI) | Not measured | 7.8% | Only MiMo-V2.5 measured |
| Legal Research Bench (Vals AI) | Not measured | 9.1% | Only MiMo-V2.5 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.1% | Only MiMo-V2.5 measured |
| τ²-Bench Telecom | 86.0% | 90.6% | MiMo-V2.5 |
| 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 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 81.6% | Only MiMo-V2.5 measured |
| MMMU Pro (Vals AI) | Not measured | 80.0% | Only MiMo-V2.5 measured |
| GPQA Diamond (Artificial Analysis run) | 91.5% | 84.9% | GPT-5.3-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 42.5% | 27.2% | GPT-5.3-Codex |
| LMArena Text: hard prompts | Not measured | 1450 | Only MiMo-V2.5 measured |
| LMArena Text: expert | Not measured | 1459 | Only MiMo-V2.5 measured |
| LMArena Text: hard prompts in English | Not measured | 1461 | Only MiMo-V2.5 measured |
| LMArena Vision: diagrams | Not measured | 1269 | Only MiMo-V2.5 measured |
| LMArena Vision: homework | Not measured | 1262 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| ProofBench | Not measured | 16.0% | Only MiMo-V2.5 measured |
| LMArena Text: math | Not measured | 1436 | Only MiMo-V2.5 measured |
| LMArena Text: mathematical work | Not measured | 1451 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 82.9% | Only MiMo-V2.5 measured |
| LegalBench (Vals AI) | Not measured | 78.9% | Only MiMo-V2.5 measured |
| CorpFin (Vals AI) | Not measured | 59.9% | Only MiMo-V2.5 measured |
| TaxEval (Vals AI) | Not measured | 71.8% | Only MiMo-V2.5 measured |
| MortgageTax (Vals AI) | Not measured | 59.3% | Only MiMo-V2.5 measured |
| MedCode (Vals AI) | Not measured | 31.9% | Only MiMo-V2.5 measured |
| LMArena Text: science | Not measured | 1440 | Only MiMo-V2.5 measured |
| LMArena Text: medicine and healthcare | Not measured | 1432 | Only MiMo-V2.5 measured |
| LMArena Text: legal and government | Not measured | 1425 | Only MiMo-V2.5 measured |
| LMArena Text: business and finance | Not measured | 1432 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index17
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1163 | 1266 | MiMo-V2.5 |
| Design Arena: UI components | 1137 | 1257 | MiMo-V2.5 |
| Design Arena: game dev | 1162 | 1249 | MiMo-V2.5 |
| Design Arena: data viz | 1164 | 1249 | MiMo-V2.5 |
| Design Arena: 3D | 1008 | 1216 | MiMo-V2.5 |
| Design Arena: all code categories | 1145 | 1260 | MiMo-V2.5 |
| Design Arena: SVG | 1129 | 1178 | MiMo-V2.5 |
| Design Arena: ASCII art | 1151 | 1141 | GPT-5.3-Codex |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-5.3-Codex | MiMo-V2.5 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 43.3% | Only MiMo-V2.5 measured |
| MedScribe (Vals AI) | Not measured | 72.2% | Only MiMo-V2.5 measured |
| Vals Multimodal Index | Not measured | 52.8% | Only MiMo-V2.5 measured |
| IFBench | 75.4% | 67.1% | GPT-5.3-Codex |
| Long context reasoning (AA-LCR) | 83.3% | 73.0% | GPT-5.3-Codex |
| LMArena Text | Not measured | 1428 | Only MiMo-V2.5 measured |
| LMArena Text: instruction following | Not measured | 1435 | Only MiMo-V2.5 measured |
| LMArena Text: creative writing | Not measured | 1393 | Only MiMo-V2.5 measured |
| LMArena Vision | Not measured | 1247 | Only MiMo-V2.5 measured |
| LMArena Text: English | Not measured | 1447 | Only MiMo-V2.5 measured |
| LMArena Text: non-English | Not measured | 1405 | Only MiMo-V2.5 measured |
| LMArena Text: Chinese | Not measured | 1469 | Only MiMo-V2.5 measured |
| LMArena Text: German | Not measured | 1421 | Only MiMo-V2.5 measured |
| LMArena Text: Japanese | Not measured | 1306 | Only MiMo-V2.5 measured |
| LMArena Text: Korean | Not measured | 1361 | Only MiMo-V2.5 measured |
| LMArena Text: French | Not measured | 1448 | Only MiMo-V2.5 measured |
| LMArena Text: Spanish | Not measured | 1414 | Only MiMo-V2.5 measured |
| LMArena Text: Russian | Not measured | 1396 | Only MiMo-V2.5 measured |
| LMArena Text: Polish | Not measured | 1413 | Only MiMo-V2.5 measured |
| LMArena Text: multi-turn | Not measured | 1445 | Only MiMo-V2.5 measured |
| LMArena Text: longer queries | Not measured | 1445 | Only MiMo-V2.5 measured |
| LMArena Text: writing and language | Not measured | 1409 | Only MiMo-V2.5 measured |
| LMArena Text: entertainment and media | Not measured | 1399 | Only MiMo-V2.5 measured |
| LMArena Vision: English | Not measured | 1259 | Only MiMo-V2.5 measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only MiMo-V2.5 measured |
| LMArena Vision: reading text | Not measured | 1256 | Only MiMo-V2.5 measured |
| LMArena Vision: humor | Not measured | 1226 | Only MiMo-V2.5 measured |
| LMArena Vision: recognizing things | Not measured | 1189 | Only MiMo-V2.5 measured |
| LMArena Vision: creative writing | Not measured | 1230 | Only MiMo-V2.5 measured |
| Neither model has a result on the other 17 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 | 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 costs less
Repository review
50K input + 3K output tokens
MiMo-V2.5 costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.5 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 |
|---|---|---|
| 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 | $0.119 |
| Output $/Mtok | $14.00 | $0.238 |
| Coding index | 29.5 | 34.0 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.59 | $0.01 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.3-Codex better than MiMo-V2.5 for coding?
Pick MiMo-V2.5 for coding: it scores 34.0 on the Vibe Coding Index against 29.5 for GPT-5.3-Codex. Pick GPT-5.3-Codex when what matters is LiveCodeBench (Vals AI) or SWE-bench Verified (Vals AI), where it leads.
Which is cheaper, GPT-5.3-Codex or MiMo-V2.5?
GPT-5.3-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; MiMo-V2.5 costs $0.119 and $0.238. Per solved SWE-bench Verified task, GPT-5.3-Codex costs $0.59 and MiMo-V2.5 $0.01.
Which has the bigger context window, GPT-5.3-Codex or MiMo-V2.5?
MiMo-V2.5, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5.3-Codex vs MiMo-V2.5 comparison based on?
22 benchmarks have a result for both models as of October 21, 2026. GPT-5.3-Codex's coding index rests on 1 independent benchmark and MiMo-V2.5's on 1. Where only one model has a result, the page says so instead of naming a winner.