GPT-5.1-Codex-Mini vs Qwen3.8 Omni Flash
Qwen3.8 Omni Flash costs 4.3x less per output token than GPT-5.1-Codex-Mini ($0.47 vs $2.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $2.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-5.1-Codex-Mini or Qwen3.8 Omni Flash: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested both yet. GPT-5.1-Codex-Mini has results on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Qwen3.8 Omni Flash has none yet. Compare them on price and context instead.
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
No call
Only GPT-5.1-Codex-Mini has a AA Intelligence Index result, 20.4.
Directional only
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
Qwen3.8 Omni Flash
Qwen3.8 Omni Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Omni Flash
GPT-5.1-Codex-Mini $0.0013, Qwen3.8 Omni Flash $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-5.1-Codex-Mini not benchmarked, Qwen3.8 Omni Flash not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- GPT-5.1-Codex-Mini $2.00 against Qwen3.8 Omni Flash $0.47 per million output tokens, 4.3x apart.
- Context window
- GPT-5.1-Codex-Mini takes 400K tokens, Qwen3.8 Omni Flash 1M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index5 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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 |
| 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 34 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 20.4 | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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 | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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 measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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 measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni 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 | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5.1-Codex-Mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| IFBench | 67.9% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Long context reasoning (AA-LCR) | 66.7% | Not measured | Only GPT-5.1-Codex-Mini measured |
| Neither model has a result on the other 44 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
Qwen3.8 Omni Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Omni Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 Omni 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 | Qwen3.8 Omni Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | November 13, 2025 | September 21, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $0.25 | $0.15 |
| Output $/Mtok | $2.00 | $0.47 |
| 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 Qwen3.8 Omni Flash for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, GPT-5.1-Codex-Mini or Qwen3.8 Omni Flash?
GPT-5.1-Codex-Mini costs $0.25 per million input tokens and $2.00 per million output tokens; Qwen3.8 Omni Flash costs $0.15 and $0.47. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5.1-Codex-Mini or Qwen3.8 Omni Flash?
Qwen3.8 Omni Flash, with 1M tokens against 400K.
Has any benchmark tested both GPT-5.1-Codex-Mini and Qwen3.8 Omni Flash?
No benchmark has tested both yet. GPT-5.1-Codex-Mini has results on LMArena WebDev, LiveCodeBench (Artificial Analysis run) and Terminal-Bench Hard (Artificial Analysis run); Qwen3.8 Omni Flash has none yet. Compare them on price and context instead. Data as of October 22, 2026.