DeepSeek V3.1 vs GPT-5.1-Codex-Mini
Updated October 21, 2026. 8 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Neither model has a coding score yet, so the two cannot be ranked on coding.
DeepSeek V3.1 has the edge where output tokens cost 1.3x less, and where what matters is AA Intelligence Index or Design Arena: game dev, where it leads.
- Shared results
- 8
- DeepSeek V3.1 only
- 2
- GPT-5.1-Codex-Mini only
- 14
- Like-for-like categories
- 2 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini leads, 20.4 against 13.7.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V3.1
DeepSeek V3.1 leads, 1123 against 1111.
Same board, both measured
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-5.1-Codex-Mini
DeepSeek V3.1 $0.0013, GPT-5.1-Codex-Mini $0.0013 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, GPT-5.1-Codex-Mini not benchmarked.
- Widest category
- Intelligence, 6.7 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: 3D: DeepSeek V3.1 1077 against GPT-5.1-Codex-Mini 988, 11.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against GPT-5.1-Codex-Mini $2.00 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V3.1 takes 164K tokens, GPT-5.1-Codex-Mini 400K.
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 | DeepSeek V3.1 | Bars | GPT-5.1-Codex-Mini | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Web design | 4 | 3 | ||
| All shared boards | 4 | 4 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1244 | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 83.6% | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 33.3% | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | Not measured | 61.6% | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: HTML | Not measured | 1253 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 20.4 | GPT-5.1-Codex-Mini |
| 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 | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 62.9% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 81.3% | Only GPT-5.1-Codex-Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 18.5% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.7 | Only GPT-5.1-Codex-Mini measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.7% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 82.0% | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 16 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index43
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | 1111 | DeepSeek V3.1 |
| Design Arena: UI components | 1085 | 1081 | DeepSeek V3.1 |
| Design Arena: game dev | 1084 | 1092 | GPT-5.1-Codex-Mini |
| Design Arena: data viz | 1095 | 1096 | GPT-5.1-Codex-Mini |
| Design Arena: 3D | 1077 | 988 | DeepSeek V3.1 |
| Design Arena: all code categories | 1112 | 1094 | DeepSeek V3.1 |
| Design Arena: SVG | 967 | 978 | GPT-5.1-Codex-Mini |
| Design Arena: ASCII art | Not measured | 1104 | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | Leader |
|---|---|---|---|
| IFBench | Not measured | 67.9% | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | Not measured | 66.7% | Only GPT-5.1-Codex-Mini measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 42 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | GPT-5.1-Codex-Mini | 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-5.1-Codex-Mini costs less
Repository review
50K input + 3K output tokens
GPT-5.1-Codex-Mini costs less
Agent loop
200K input + 10K output tokens
GPT-5.1-Codex-Mini 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 | DeepSeek V3.1 | OpenAI GPT-5.1-Codex-Mini |
|---|---|---|
| Maker | DeepSeek | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | November 13, 2025 |
| Context window | 164K | 400K |
| Input $/Mtok | $0.55 | $0.25 |
| Output $/Mtok | $1.50 | $2.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than GPT-5.1-Codex-Mini for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. DeepSeek V3.1 has the edge where output tokens cost 1.3x less, and where what matters is AA Intelligence Index or Design Arena: game dev, where it leads.
Which is cheaper, DeepSeek V3.1 or GPT-5.1-Codex-Mini?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 per million output tokens; GPT-5.1-Codex-Mini costs $0.25 and $2.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.1 or GPT-5.1-Codex-Mini?
GPT-5.1-Codex-Mini, with 400K tokens against 164K.
How many benchmarks is the DeepSeek V3.1 vs GPT-5.1-Codex-Mini comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and GPT-5.1-Codex-Mini's on 1. Where only one model has a result, the page says so instead of naming a winner.