GPT-3.5 Turbo vs MiniMax M2.1
MiniMax M2.1 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 20.9 to 5.5, and MiniMax M2.1 costs 1.3x less per output token ($1.20 vs $1.50 per million). Updated October 23, 2026; 6 benchmarks have a result for both.
GPT-3.5 Turbo or MiniMax M2.1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for work measured by AA Intelligence Index or GPQA Diamond, where it leads.
- Shared results
- 6
- GPT-3.5 Turbo only
- 7
- MiniMax M2.1 only
- 28
- Like-for-like categories
- 3 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
MiniMax M2.1
MiniMax M2.1 leads, 20.9 against 5.5.
Same board, both measured
Reasoning
GPQA Diamond
MiniMax M2.1
MiniMax M2.1 leads, 78.5% against 30.6%.
Same board, both measured
Math
AIME
No call
Only MiniMax M2.1 has a AIME result, 77.9%.
Directional only
Knowledge
MMLU Pro
No call
Only MiniMax M2.1 has a MMLU Pro result, 87.0%.
Directional only
Web design
Design Arena: websites
No call
Only MiniMax M2.1 has a Design Arena: websites result, 1201.
Directional only
Long documents
Listed context window
MiniMax M2.1
MiniMax M2.1 takes 205K tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M2.1
GPT-3.5 Turbo $0.0013, MiniMax M2.1 $0.0009 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only MiniMax M2.1 has the number, $0.59.
Directional only
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, MiniMax M2.1 not benchmarked.
- Widest category
- Reasoning, 50.6 points apart on average over 2 shared benchmarks.
- Widest single gap
- GPQA Diamond (Artificial Analysis run): GPT-3.5 Turbo 29.7% against MiniMax M2.1 83.0%, 53.3 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against MiniMax M2.1 $1.20 per million output tokens, 1.3x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, MiniMax M2.1 205K.
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 | MiniMax M2.1 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 2 | ||
| Knowledge | 0 | 3 | ||
| All shared boards | 0 | 6 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LiveCodeBench (Vals AI) | Not measured | 81.8% | Only MiniMax M2.1 measured |
| SWE-bench Verified (Vals AI) | Not measured | 74.8% | Only MiniMax M2.1 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 81.0% | Only MiniMax M2.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 28.8% | Only MiniMax M2.1 measured |
| Terminal-Bench | Not measured | 36.6% | Only MiniMax M2.1 measured |
| ALE-Bench | Not measured | 623.8 | Only MiniMax M2.1 measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 20.9 | MiniMax M2.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 85.4% | Only MiniMax M2.1 measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | 78.5% | MiniMax M2.1 |
| GPQA Diamond (Artificial Analysis run) | 29.7% | 83.0% | MiniMax M2.1 |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 23.2% | Only MiniMax M2.1 measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| AIME | Not measured | 77.9% | Only MiniMax M2.1 measured |
| MATH 500 (Vals AI) | Not measured | 89.0% | Only MiniMax M2.1 measured |
| MGSM (Vals AI) | Not measured | 87.8% | Only MiniMax M2.1 measured |
| AA Math Index | Not measured | 82.7 | Only MiniMax M2.1 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 82.7% | Only MiniMax M2.1 measured |
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 87.0% | Only MiniMax M2.1 measured |
| LegalBench (Vals AI) | 64.4% | 75.5% | MiniMax M2.1 |
| CaseLaw (Vals AI) | Not measured | 55.8% | Only MiniMax M2.1 measured |
| CorpFin (Vals AI) | Not measured | 58.9% | Only MiniMax M2.1 measured |
| TaxEval (Vals AI) | Not measured | 66.3% | Only MiniMax M2.1 measured |
| MedQA (Vals AI) | 58.5% | 91.2% | MiniMax M2.1 |
| MedCode (Vals AI) | Not measured | 34.1% | Only MiniMax M2.1 measured |
| MMLU Pro (Artificial Analysis run) | 46.2% | 87.5% | MiniMax M2.1 |
| MMLU | 68.5% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1201 | Only MiniMax M2.1 measured |
| Design Arena: UI components | Not measured | 1220 | Only MiniMax M2.1 measured |
| Design Arena: game dev | Not measured | 1134 | Only MiniMax M2.1 measured |
| Design Arena: data viz | Not measured | 1207 | Only MiniMax M2.1 measured |
| Design Arena: 3D | Not measured | 1166 | Only MiniMax M2.1 measured |
| Design Arena: all code categories | Not measured | 1190 | Only MiniMax M2.1 measured |
| Design Arena: SVG | Not measured | 1131 | Only MiniMax M2.1 measured |
| Neither model has a result on the other 2 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 80.8% | Only MiniMax M2.1 measured |
| IFBench | Not measured | 69.9% | Only MiniMax M2.1 measured |
| Long context reasoning (AA-LCR) | Not measured | 67.7% | Only MiniMax M2.1 measured |
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M2.1 | 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
MiniMax M2.1 costs less
Repository review
50K input + 3K output tokens
MiniMax M2.1 costs less
Agent loop
200K input + 10K output tokens
MiniMax M2.1 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 | MiniMax M2.1 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | Open weights |
| Released | May 28, 2023 | December 23, 2025 |
| Context window | 16K | 205K |
| Input $/Mtok | $0.50 | $0.30 |
| Output $/Mtok | $1.50 | $1.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | $0.59 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than MiniMax M2.1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for work measured by AA Intelligence Index or GPQA Diamond, where it leads.
Which is cheaper, GPT-3.5 Turbo or MiniMax M2.1?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; MiniMax M2.1 costs $0.30 and $1.20. Cost per solved task is known for MiniMax M2.1 only.
Which has the bigger context window, GPT-3.5 Turbo or MiniMax M2.1?
MiniMax M2.1, with 205K tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs MiniMax M2.1 comparison based on?
6 benchmarks have a result for both models as of October 23, 2026. GPT-3.5 Turbo's coding index rests on 0 independent benchmarks and MiniMax M2.1's on 1. Where only one model has a result, the page says so instead of naming a winner.