GPT-3.5 Turbo vs MiMo-V2.5-Pro
MiMo-V2.5-Pro leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 26.0 to 5.5, and MiMo-V2.5-Pro costs 1.6x less per output token ($0.9152 vs $1.50 per million). Updated October 22, 2026; 7 benchmarks have a result for both.
GPT-3.5 Turbo 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-3.5 Turbo has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 7
- GPT-3.5 Turbo only
- 6
- MiMo-V2.5-Pro only
- 64
- Like-for-like categories
- 5 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 5.5.
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 30.6%.
Same board, both measured
Knowledge
MMLU Pro
No call
Only MiMo-V2.5-Pro has a MMLU Pro result, 84.6%.
Directional only
Web design
Design Arena: websites
No call
Only MiMo-V2.5-Pro has a Design Arena: websites result, 1266.
Directional only
Long documents
Listed context window
MiMo-V2.5-Pro
MiMo-V2.5-Pro takes 1.05M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.5-Pro
GPT-3.5 Turbo $0.0013, MiMo-V2.5-Pro $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiMo-V2.5-Pro has the number, $0.03.
Directional only
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, MiMo-V2.5-Pro 31.6 from 4 benchmarks.
- Widest category
- Reasoning, 54.5 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against MiMo-V2.5-Pro 86.6%, 56.9 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against MiMo-V2.5-Pro $0.9152 per million output tokens, 1.6x apart.
- Context window
- GPT-3.5 Turbo takes 16K 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-3.5 Turbo | Bars | MiMo-V2.5-Pro | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 1 | ||
| General | 0 | 2 | ||
| All shared boards | 0 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1480 | Only MiMo-V2.5-Pro measured |
| AA Coding Index | 10.7 | 60.2 | MiMo-V2.5-Pro |
| LiveCodeBench (Vals AI) | Not measured | 81.3% | Only MiMo-V2.5-Pro measured |
| 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 |
| SciCode | Not measured | 50.6% | Only MiMo-V2.5-Pro measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 43.2% | Only MiMo-V2.5-Pro measured |
| ALE-Bench | Not measured | 899.8 | Only MiMo-V2.5-Pro measured |
| 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 | Not measured | 1469 | Only MiMo-V2.5-Pro measured |
| LMArena WebDev: HTML | Not measured | 1473 | Only MiMo-V2.5-Pro measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 26.0 | MiMo-V2.5-Pro |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-3.5 Turbo | 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 | Not measured | 94.2% | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | 82.6% | MiMo-V2.5-Pro |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 86.6% | MiMo-V2.5-Pro |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 35.7% | Only MiMo-V2.5-Pro measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| 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 index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| ProofBench | Not measured | 22.0% | Only MiMo-V2.5-Pro measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo 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 | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.6% | Only MiMo-V2.5-Pro measured |
| LegalBench (Vals AI) | 64.4% | 77.1% | MiMo-V2.5-Pro |
| 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 |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo measured |
| MedCode (Vals AI) | Not measured | 32.5% | Only MiMo-V2.5-Pro measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | Not measured | Only GPT-3.5 Turbo measured |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo 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 8 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1266 | Only MiMo-V2.5-Pro measured |
| Design Arena: UI components | Not measured | 1246 | Only MiMo-V2.5-Pro measured |
| Design Arena: game dev | Not measured | 1261 | Only MiMo-V2.5-Pro measured |
| Design Arena: data viz | Not measured | 1257 | Only MiMo-V2.5-Pro measured |
| Design Arena: 3D | Not measured | 1243 | Only MiMo-V2.5-Pro measured |
| Design Arena: all code categories | Not measured | 1265 | Only MiMo-V2.5-Pro measured |
| Design Arena: SVG | Not measured | 1182 | 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 index02
| Benchmark | GPT-3.5 Turbo | MiMo-V2.5-Pro | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 83.7% | Only MiMo-V2.5-Pro measured |
| IFBench | Not measured | 79.9% | Only MiMo-V2.5-Pro measured |
| Long context reasoning (AA-LCR) | Not measured | 79.7% | Only MiMo-V2.5-Pro measured |
| DTBench | 48.5% | 84.5% | MiMo-V2.5-Pro |
| LMCA | 9.7% | 29.5% | MiMo-V2.5-Pro |
| 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 |
| EQ-Bench 4 | Not measured | 1208 | Only MiMo-V2.5-Pro measured |
| Neither model has a result on the other 25 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | 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-3.5 Turbo | Xiaomi MiMo-V2.5-Pro |
|---|---|---|
| Maker | OpenAI | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | April 22, 2026 |
| Context window | 16K | 1.05M |
| Input $/Mtok | $0.50 | $0.4575 |
| Output $/Mtok | $1.50 | $0.9152 |
| Coding index | Not benchmarked | 31.6 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | $0.03 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo 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-3.5 Turbo has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-3.5 Turbo or MiMo-V2.5-Pro?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; MiMo-V2.5-Pro costs $0.4575 and $0.9152. Cost per solved task is known for MiMo-V2.5-Pro only.
Which has the bigger context window, GPT-3.5 Turbo or MiMo-V2.5-Pro?
MiMo-V2.5-Pro, with 1.05M tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs MiMo-V2.5-Pro comparison based on?
7 benchmarks have a result for both models as of October 22, 2026. GPT-3.5 Turbo'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.