GPT-3.5 Turbo Instruct vs MiMo-V2.6-Pro
MiMo-V2.6-Pro costs 2.3x less per output token than GPT-3.5 Turbo Instruct ($0.87 vs $2.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- 65.4
- Overall
- 68.4
- Output $/M
- $0.87
GPT-3.5 Turbo Instruct or MiMo-V2.6-Pro: which to pick
Only MiMo-V2.6-Pro has a coding score, 65.4 on the Vibe Coding Index; GPT-3.5 Turbo Instruct has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; MiMo-V2.6-Pro on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI). Compare them on price and context instead.
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-V2.6-Pro has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only MiMo-V2.6-Pro has a AA Intelligence Index result, 46.3.
Directional only
Web design
Design Arena: websites
No call
Only MiMo-V2.6-Pro has a Design Arena: websites result, 1308.
Directional only
General
Vals Index
No call
Only MiMo-V2.6-Pro has a Vals Index result, 57.8%.
Directional only
Long documents
Listed context window
MiMo-V2.6-Pro
MiMo-V2.6-Pro takes 1.05M tokens against 4K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiMo-V2.6-Pro
GPT-3.5 Turbo Instruct $0.0025, MiMo-V2.6-Pro $0.0009 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo Instruct not benchmarked, MiMo-V2.6-Pro 65.4 from 4 benchmarks.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-3.5 Turbo Instruct $2.00 against MiMo-V2.6-Pro $0.87 per million output tokens, 2.3x apart.
- Context window
- GPT-3.5 Turbo Instruct takes 4K tokens, MiMo-V2.6-Pro 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index17 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 81.5% | Only MiMo-V2.6-Pro measured |
| LMArena WebDev | Not measured | 1625 | Only MiMo-V2.6-Pro measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 31.3% | Only MiMo-V2.6-Pro measured |
| Vibe Code Bench (Vals AI) | Not measured | 85.2% | Only MiMo-V2.6-Pro measured |
| Code migration (Vals AI) | Not measured | 43.0% | Only MiMo-V2.6-Pro measured |
| ProgramBench (Vals AI) | Not measured | 0.5% | Only MiMo-V2.6-Pro measured |
| IOI (Vals AI) | Not measured | 39.3% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.8% | Only MiMo-V2.6-Pro measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 41.3% | Only MiMo-V2.6-Pro measured |
| CyberBench (Vals AI) | Not measured | 72.9% | Only MiMo-V2.6-Pro measured |
| SciCode | Not measured | 60.9% | Only MiMo-V2.6-Pro measured |
| ALE-Bench | Not measured | 1158.1 | Only MiMo-V2.6-Pro measured |
| LMArena Text: coding | Not measured | 1533 | Only MiMo-V2.6-Pro measured |
| LMArena Text: software and IT | Not measured | 1528 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: React | Not measured | 1639 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: HTML | Not measured | 1616 | Only MiMo-V2.6-Pro measured |
| LMArena WebDev: from an image | Not measured | 1598 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 46.3 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1290 | Only MiMo-V2.6-Pro measured |
| LiveBench research | Not measured | 76.7% | Only MiMo-V2.6-Pro measured |
| LiveBench browser | Not measured | 88.0% | Only MiMo-V2.6-Pro measured |
| LiveBench long agentic | Not measured | 47.5% | Only MiMo-V2.6-Pro measured |
| SREBench (Vals AI) | Not measured | 3.0% | Only MiMo-V2.6-Pro measured |
| Finance Agent (Vals AI) | Not measured | 57.3% | Only MiMo-V2.6-Pro measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.4% | Only MiMo-V2.6-Pro measured |
| Legal Research Bench (Vals AI) | Not measured | 47.1% | Only MiMo-V2.6-Pro measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 13.8% | Only MiMo-V2.6-Pro measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 62.9% | Only MiMo-V2.6-Pro measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only MiMo-V2.6-Pro measured |
| APEX-Agents | Not measured | 54.4% | Only MiMo-V2.6-Pro measured |
| METR time horizons | 21.5% | Not measured | Only GPT-3.5 Turbo Instruct measured |
| Neither model has a result on the other 28 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index8 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 79.6% | Only MiMo-V2.6-Pro measured |
| MysteryMechanism (Vals AI) | Not measured | 15.3% | Only MiMo-V2.6-Pro measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 49.4% | Only MiMo-V2.6-Pro measured |
| LMArena Text: hard prompts | Not measured | 1511 | Only MiMo-V2.6-Pro measured |
| LMArena Text: expert | Not measured | 1541 | Only MiMo-V2.6-Pro measured |
| LMArena Text: hard prompts in English | Not measured | 1520 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: diagrams | Not measured | 1281 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: homework | Not measured | 1264 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 14 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| ProofBench | Not measured | 70.0% | Only MiMo-V2.6-Pro measured |
| LMArena Text: math | Not measured | 1500 | Only MiMo-V2.6-Pro measured |
| LMArena Text: mathematical work | Not measured | 1511 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| MedCode (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| LMArena Text: science | Not measured | 1506 | Only MiMo-V2.6-Pro measured |
| LMArena Text: medicine and healthcare | Not measured | 1497 | Only MiMo-V2.6-Pro measured |
| LMArena Text: legal and government | Not measured | 1485 | Only MiMo-V2.6-Pro measured |
| LMArena Text: business and finance | Not measured | 1468 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1308 | Only MiMo-V2.6-Pro measured |
| Design Arena: UI components | Not measured | 1340 | Only MiMo-V2.6-Pro measured |
| Design Arena: game dev | Not measured | 1314 | Only MiMo-V2.6-Pro measured |
| Design Arena: data viz | Not measured | 1336 | Only MiMo-V2.6-Pro measured |
| Design Arena: 3D | Not measured | 1330 | Only MiMo-V2.6-Pro measured |
| Design Arena: all code categories | Not measured | 1317 | Only MiMo-V2.6-Pro measured |
| LiveBench design | Not measured | 78.5% | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index20 boards measured
| Benchmark | GPT-3.5 Turbo Instruct | MiMo-V2.6-Pro | Leader |
|---|---|---|---|
| Vals Index | Not measured | 57.8% | Only MiMo-V2.6-Pro measured |
| LiveBench data analysis | Not measured | 72.3% | Only MiMo-V2.6-Pro measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.9% | Only MiMo-V2.6-Pro measured |
| SAGE (Vals AI) | Not measured | 45.0% | Only MiMo-V2.6-Pro measured |
| MedScribe (Vals AI) | Not measured | 88.3% | Only MiMo-V2.6-Pro measured |
| Long context reasoning (AA-LCR) | Not measured | 86.3% | Only MiMo-V2.6-Pro measured |
| LMArena Text | Not measured | 1491 | Only MiMo-V2.6-Pro measured |
| LMArena Text: instruction following | Not measured | 1491 | Only MiMo-V2.6-Pro measured |
| LMArena Text: creative writing | Not measured | 1466 | Only MiMo-V2.6-Pro measured |
| LMArena Vision | Not measured | 1263 | Only MiMo-V2.6-Pro measured |
| LMArena Text: English | Not measured | 1504 | Only MiMo-V2.6-Pro measured |
| LMArena Text: non-English | Not measured | 1474 | Only MiMo-V2.6-Pro measured |
| LMArena Text: Chinese | Not measured | 1529 | Only MiMo-V2.6-Pro measured |
| LMArena Text: Russian | Not measured | 1479 | Only MiMo-V2.6-Pro measured |
| LMArena Text: multi-turn | Not measured | 1466 | Only MiMo-V2.6-Pro measured |
| LMArena Text: longer queries | Not measured | 1501 | Only MiMo-V2.6-Pro measured |
| LMArena Text: writing and language | Not measured | 1470 | Only MiMo-V2.6-Pro measured |
| LMArena Text: entertainment and media | Not measured | 1465 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: English | Not measured | 1268 | Only MiMo-V2.6-Pro measured |
| LMArena Vision: reading text | Not measured | 1270 | Only MiMo-V2.6-Pro measured |
| Neither model has a result on the other 26 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-V2.6-Pro costs less
Repository review
50K input + 3K output tokens
MiMo-V2.6-Pro costs less
Agent loop
200K input + 10K output tokens
MiMo-V2.6-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 Instruct | Xiaomi MiMo-V2.6-Pro |
|---|---|---|
| Maker | OpenAI | Xiaomi |
| Weights | No public weights listed | Open weights |
| Released | September 28, 2023 | September 21, 2026 |
| Context window | 4K | 1.05M |
| Input $/Mtok | $1.50 | $0.435 |
| Output $/Mtok | $2.00 | $0.87 |
| Coding index | Not benchmarked | 65.4 |
| Independent measurements behind it | 0 | 4 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo Instruct better than MiMo-V2.6-Pro for coding?
Only MiMo-V2.6-Pro has a coding score, 65.4 on the Vibe Coding Index; GPT-3.5 Turbo Instruct 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 Instruct or MiMo-V2.6-Pro?
GPT-3.5 Turbo Instruct costs $1.50 per million input tokens and $2.00 per million output tokens; MiMo-V2.6-Pro costs $0.435 and $0.87. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo Instruct or MiMo-V2.6-Pro?
MiMo-V2.6-Pro, with 1.05M tokens against 4K.
Has any benchmark tested both GPT-3.5 Turbo Instruct and MiMo-V2.6-Pro?
No benchmark has tested both yet. GPT-3.5 Turbo Instruct has results on METR time horizons; MiMo-V2.6-Pro on LiveBench coding, LMArena WebDev and Terminal-Bench 4 (Vals AI). Compare them on price and context instead. Data as of October 22, 2026.