GPT-3.5 Turbo vs MiniMax M1
MiniMax M1 leads GPT-3.5 Turbo on the Artificial Analysis Intelligence Index, 11.7 to 5.5, and GPT-3.5 Turbo costs 1.5x less per output token ($1.50 vs $2.20 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-3.5 Turbo or MiniMax M1: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-3.5 Turbo for output tokens 1.5x cheaper ($1.50 vs $2.20 per million) and work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- GPT-3.5 Turbo only
- 12
- MiniMax M1 only
- 28
- Like-for-like categories
- 1 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 M1
MiniMax M1 leads, 11.7 against 5.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-3.5 Turbo has a GPQA Diamond result, 30.6%.
Directional only
Long documents
Listed context window
MiniMax M1
MiniMax M1 takes 1M tokens against 16K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-3.5 Turbo
GPT-3.5 Turbo $0.0013, MiniMax M1 $0.0015 per turn.
List prices
At a glance
- Coding index
- GPT-3.5 Turbo not benchmarked, MiniMax M1 not benchmarked.
- Widest category
- Intelligence, 6.2 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-3.5 Turbo 5.5 against MiniMax M1 11.7, 6.2 points apart on a 0 to 100 scale.
- Output price
- GPT-3.5 Turbo $1.50 against MiniMax M1 $2.20 per million output tokens, 1.5x apart.
- Context window
- GPT-3.5 Turbo takes 16K tokens, MiniMax M1 1M.
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 M1 | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index4 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| AA Coding Index | 10.7 | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: coding | Not measured | 1359 | Only MiniMax M1 measured |
| LMArena Text: software and IT | Not measured | 1359 | Only MiniMax M1 measured |
| SciCode | 0.0% | Not measured | Only GPT-3.5 Turbo measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 5.5 | 11.7 | MiniMax M1 |
| Neither model has a result on the other 1 board here | |||
Agentic workNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| Neither model has a result on 42 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| GPQA Diamond | 30.6% | Not measured | Only GPT-3.5 Turbo measured |
| GPQA Diamond (Artificial Analysis run) | 29.7% | Not measured | Only GPT-3.5 Turbo measured |
| BIG-Bench Hard | 61.6% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: hard prompts | Not measured | 1339 | Only MiniMax M1 measured |
| LMArena Text: expert | Not measured | 1317 | Only MiniMax M1 measured |
| LMArena Text: hard prompts in English | Not measured | 1353 | Only MiniMax M1 measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| GSM8K | 57.8% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text: math | Not measured | 1361 | Only MiniMax M1 measured |
| LMArena Text: mathematical work | Not measured | 1363 | Only MiniMax M1 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| LegalBench (Vals AI) | 64.4% | Not measured | Only GPT-3.5 Turbo measured |
| MedQA (Vals AI) | 58.5% | Not measured | Only GPT-3.5 Turbo 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 | 1354 | Only MiniMax M1 measured |
| LMArena Text: medicine and healthcare | Not measured | 1355 | Only MiniMax M1 measured |
| LMArena Text: legal and government | Not measured | 1337 | Only MiniMax M1 measured |
| LMArena Text: business and finance | Not measured | 1319 | Only MiniMax M1 measured |
| Neither model has a result on the other 12 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| Neither model has a result on 9 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | Leader |
|---|---|---|---|
| DTBench | 48.5% | Not measured | Only GPT-3.5 Turbo measured |
| LMCA | 9.7% | Not measured | Only GPT-3.5 Turbo measured |
| LMArena Text | Not measured | 1343 | Only MiniMax M1 measured |
| LMArena Text: instruction following | Not measured | 1312 | Only MiniMax M1 measured |
| LMArena Text: creative writing | Not measured | 1297 | Only MiniMax M1 measured |
| LMArena Text: English | Not measured | 1363 | Only MiniMax M1 measured |
| LMArena Text: non-English | Not measured | 1319 | Only MiniMax M1 measured |
| LMArena Text: Chinese | Not measured | 1360 | Only MiniMax M1 measured |
| LMArena Text: German | Not measured | 1350 | Only MiniMax M1 measured |
| LMArena Text: Japanese | Not measured | 1217 | Only MiniMax M1 measured |
| LMArena Text: Korean | Not measured | 1266 | Only MiniMax M1 measured |
| LMArena Text: French | Not measured | 1370 | Only MiniMax M1 measured |
| LMArena Text: Spanish | Not measured | 1353 | Only MiniMax M1 measured |
| LMArena Text: Russian | Not measured | 1329 | Only MiniMax M1 measured |
| LMArena Text: Polish | Not measured | 1329 | Only MiniMax M1 measured |
| LMArena Text: multi-turn | Not measured | 1335 | Only MiniMax M1 measured |
| LMArena Text: longer queries | Not measured | 1326 | Only MiniMax M1 measured |
| LMArena Text: writing and language | Not measured | 1311 | Only MiniMax M1 measured |
| LMArena Text: entertainment and media | Not measured | 1303 | Only MiniMax M1 measured |
| Neither model has a result on the other 29 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-3.5 Turbo | MiniMax M1 | 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
GPT-3.5 Turbo costs less
Repository review
50K input + 3K output tokens
MiniMax M1 costs less
Agent loop
200K input + 10K output tokens
MiniMax M1 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 M1 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | No public weights listed |
| Released | May 28, 2023 | June 17, 2025 |
| Context window | 16K | 1M |
| Input $/Mtok | $0.50 | $0.40 |
| Output $/Mtok | $1.50 | $2.20 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-3.5 Turbo better than MiniMax M1 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-3.5 Turbo for output tokens 1.5x cheaper ($1.50 vs $2.20 per million) and work measured by AA Intelligence Index, where it leads.
Which is cheaper, GPT-3.5 Turbo or MiniMax M1?
GPT-3.5 Turbo costs $0.50 per million input tokens and $1.50 per million output tokens; MiniMax M1 costs $0.40 and $2.20. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-3.5 Turbo or MiniMax M1?
MiniMax M1, with 1M tokens against 16K.
How many benchmarks is the GPT-3.5 Turbo vs MiniMax M1 comparison based on?
1 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 MiniMax M1's on 0. Where only one model has a result, the page says so instead of naming a winner.