GPT-5.2-Codex vs MiniMax M1
GPT-5.2-Codex leads MiniMax M1 on the Artificial Analysis Intelligence Index, 28.5 to 11.7, and MiniMax M1 costs 6.4x less per output token ($2.20 vs $14.00 per million). Updated October 22, 2026; 1 benchmarks have a result for both.
GPT-5.2-Codex or MiniMax M1: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding.
Pick MiniMax M1 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and a larger context window (1M against 400K tokens).
- Shared results
- 1
- GPT-5.2-Codex only
- 21
- 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
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 11.7.
Same board, both measured
Long documents
Listed context window
MiniMax M1
MiniMax M1 takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
MiniMax M1
GPT-5.2-Codex $0.0088, MiniMax M1 $0.0015 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GPT-5.2-Codex 45.4 from 3 benchmarks, MiniMax M1 not benchmarked.
- Widest category
- Intelligence, 16.8 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-5.2-Codex 28.5 against MiniMax M1 11.7, 16.8 points apart on a 0 to 100 scale.
- Output price
- GPT-5.2-Codex $14.00 against MiniMax M1 $2.20 per million output tokens, 6.4x apart.
- Context window
- GPT-5.2-Codex takes 400K 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-5.2-Codex | Bars | MiniMax M1 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| All shared boards | 1 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index13 boards, none shared
| Benchmark | GPT-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | 66.3% | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev | 1339 | Not measured | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | 88.0% | Not measured | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | 72.4% | Not measured | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | 37.9% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.1% | Not measured | Only GPT-5.2-Codex measured |
| Terminal-Bench | 66.5% | Not measured | Only GPT-5.2-Codex measured |
| ALE-Bench | 1299.9 | Not measured | Only GPT-5.2-Codex measured |
| LMArena Text: coding | Not measured | 1359 | Only MiniMax M1 measured |
| LMArena Text: software and IT | Not measured | 1359 | Only MiniMax M1 measured |
| LMArena WebDev: React | 1329 | Not measured | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | 1348 | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 26 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| AA Intelligence Index | 28.5 | 11.7 | GPT-5.2-Codex |
| 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-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1051 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 993 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | 1090 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | 1176 | Not measured | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 92.1% | Not measured | Only GPT-5.2-Codex measured |
| Neither model has a result on the other 35 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 89.9% | Not measured | Only GPT-5.2-Codex measured |
| Humanity's Last Exam (Artificial Analysis run) | 35.7% | Not measured | Only GPT-5.2-Codex 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 index2 boards, none shared
| Benchmark | GPT-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| 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 index4 boards, none shared
| Benchmark | GPT-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| 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 14 boards here | |||
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | 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-5.2-Codex | MiniMax M1 | Leader |
|---|---|---|---|
| IFBench | 77.6% | Not measured | Only GPT-5.2-Codex measured |
| Long context reasoning (AA-LCR) | 82.3% | Not measured | Only GPT-5.2-Codex 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 27 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.2-Codex | 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
MiniMax M1 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-5.2-Codex | MiniMax M1 |
|---|---|---|
| Maker | OpenAI | MiniMax |
| Weights | No public weights listed | No public weights listed |
| Released | January 14, 2026 | June 17, 2025 |
| Context window | 400K | 1M |
| Input $/Mtok | $1.75 | $0.40 |
| Output $/Mtok | $14.00 | $2.20 |
| Coding index | 45.4 | Not benchmarked |
| Independent measurements behind it | 3 | 0 |
| Cost per solved task | $0.43 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.2-Codex better than MiniMax M1 for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; MiniMax M1 has none, so the two cannot be ranked on coding. Pick MiniMax M1 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and a larger context window (1M against 400K tokens).
Which is cheaper, GPT-5.2-Codex or MiniMax M1?
GPT-5.2-Codex costs $1.75 per million input tokens and $14.00 per million output tokens; MiniMax M1 costs $0.40 and $2.20. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GPT-5.2-Codex or MiniMax M1?
MiniMax M1, with 1M tokens against 400K.
How many benchmarks is the GPT-5.2-Codex vs MiniMax M1 comparison based on?
1 benchmarks have a result for both models as of October 22, 2026. GPT-5.2-Codex's coding index rests on 3 independent benchmarks and MiniMax M1's on 0. Where only one model has a result, the page says so instead of naming a winner.