GPT-5.1-Codex-Mini vs Ling 3.1 Flash
Ling 3.1 Flash leads GPT-5.1-Codex-Mini on the Artificial Analysis Intelligence Index, 41.1 to 20.4, and Ling 3.1 Flash costs 8.9x less per output token ($0.225 vs $2.00 per million). Updated October 22, 2026; 3 benchmarks have a result for both.
GPT-5.1-Codex-Mini or Ling 3.1 Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5.1-Codex-Mini for a larger context window (400K against 262K tokens) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
- Shared results
- 3
- GPT-5.1-Codex-Mini only
- 19
- Ling 3.1 Flash only
- 1
- 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
Ling 3.1 Flash
Ling 3.1 Flash leads, 41.1 against 20.4.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-5.1-Codex-Mini has a Design Arena: websites result, 1111.
Directional only
Long documents
Listed context window
GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Ling 3.1 Flash
GPT-5.1-Codex-Mini $0.0013, Ling 3.1 Flash $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Ling 3.1 Flash not benchmarked.
- Widest category
- Reasoning, 20.9 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): GPT-5.1-Codex-Mini 18.5% against Ling 3.1 Flash 39.4%, 20.9 points apart on a 0 to 100 scale.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Ling 3.1 Flash $0.225 per million output tokens, 8.9x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Ling 3.1 Flash 262K.
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-5.1-Codex-Mini | Bars | Ling 3.1 Flash | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index6 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| LMArena WebDev | 1244 | Not measured | Only GPT-5.1-Codex-Mini measured |
| LiveCodeBench (Artificial Analysis run) | 83.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| SciCode | Not measured | 54.1% | Only Ling 3.1 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Terminal-Bench | 61.6% | Not measured | Only GPT-5.1-Codex-Mini measured |
| LMArena WebDev: HTML | 1253 | Not measured | 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 | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | 41.1 | Ling 3.1 Flash |
| 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-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 62.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 81.3% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 18.5% | 39.4% | Ling 3.1 Flash |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| AA Math Index | 91.7 | Not measured | Only GPT-5.1-Codex-Mini measured |
| AIME 2025 (Artificial Analysis run) | 91.7% | Not measured | 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 | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 82.0% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1111 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: UI components | 1078 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: game dev | 1092 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: data viz | 1097 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: 3D | 988 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: all code categories | 1093 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: SVG | 978 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Design Arena: ASCII art | 1105 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | Leader |
|---|---|---|---|
| IFBench | 67.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | 66.7% | 83.0% | Ling 3.1 Flash |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5.1-Codex-Mini | Ling 3.1 Flash | 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
Ling 3.1 Flash costs less
Repository review
50K input + 3K output tokens
Ling 3.1 Flash costs less
Agent loop
200K input + 10K output tokens
Ling 3.1 Flash 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-5.1-Codex-Mini | inclusionAI Ling 3.1 Flash |
|---|---|---|
| Maker | OpenAI | inclusionAI |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | October 2, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $0.25 | $0.075 |
| Output $/Mtok | $2.00 | $0.225 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5.1-Codex-Mini better than Ling 3.1 Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5.1-Codex-Mini for a larger context window (400K against 262K tokens) and work measured by AA Intelligence Index or Humanity's Last Exam (Artificial Analysis run), where it leads.
Which is cheaper, GPT-5.1-Codex-Mini or Ling 3.1 Flash?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Ling 3.1 Flash costs $0.075 and $0.225. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Ling 3.1 Flash?
GPT-5.1-Codex-Mini, with 400K tokens against 262K.
How many benchmarks is the GPT-5.1-Codex-Mini vs Ling 3.1 Flash comparison based on?
3 benchmarks have a result for both models as of October 22, 2026. GPT-5.1-Codex-Mini's coding index rests on 1 independent benchmark and Ling 3.1 Flash's on 0. Where only one model has a result, the page says so instead of naming a winner.