GPT-4o-mini vs Qwen3.8 Omni Flash
Qwen3.8 Omni Flash costs 1.3x less per output token than GPT-4o-mini ($0.47 vs $0.60 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.60
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-4o-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-4o-mini has results on AA Coding Index, LiveCodeBench (Vals AI) and LiveCodeBench (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-4o-mini has a AA Intelligence Index result, 6.7.
Directional only
Reasoning
GPQA Diamond
No call
Only GPT-4o-mini has a GPQA Diamond result, 44.2%.
Directional only
Math
AIME
No call
Only GPT-4o-mini has a AIME result, 11.5%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o-mini has a MMLU Pro result, 62.7%.
Directional only
Long documents
Listed context window
Qwen3.8 Omni Flash
Qwen3.8 Omni Flash takes 1M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Omni Flash
GPT-4o-mini $0.0004, Qwen3.8 Omni Flash $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4o-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-4o-mini $0.60 against Qwen3.8 Omni Flash $0.47 per million output tokens, 1.3x apart.
- Context window
- GPT-4o-mini takes 128K 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 Index4 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Coding Index | 11.4 | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Vals AI) | 26.4% | Not measured | Only GPT-4o-mini measured |
| LiveCodeBench (Artificial Analysis run) | 23.4% | Not measured | Only GPT-4o-mini measured |
| SciCode | 1.5% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 39 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.7 | Not measured | Only GPT-4o-mini measured |
| Epoch Capabilities Index | 126.5 | Not measured | Only GPT-4o-mini measured |
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 44.2% | Not measured | Only GPT-4o-mini measured |
| MMMU Pro (Vals AI) | 56.6% | Not measured | Only GPT-4o-mini measured |
| GPQA Diamond (Artificial Analysis run) | 42.6% | Not measured | Only GPT-4o-mini measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only GPT-4o-mini measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o-mini measured |
| Kagi LLM Benchmark | 28.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| AIME | 11.5% | Not measured | Only GPT-4o-mini measured |
| MATH 500 (Vals AI) | 72.6% | Not measured | Only GPT-4o-mini measured |
| MGSM (Vals AI) | 86.2% | Not measured | Only GPT-4o-mini measured |
| AA Math Index | 14.7 | Not measured | Only GPT-4o-mini measured |
| AIME 2025 (Artificial Analysis run) | 14.7% | Not measured | Only GPT-4o-mini measured |
| GSM8K | 91.3% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 8 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| MMLU Pro | 62.7% | Not measured | Only GPT-4o-mini measured |
| CorpFin (Vals AI) | 45.5% | Not measured | Only GPT-4o-mini measured |
| TaxEval (Vals AI) | 60.5% | Not measured | Only GPT-4o-mini measured |
| MortgageTax (Vals AI) | 54.5% | Not measured | Only GPT-4o-mini measured |
| MedQA (Vals AI) | 72.4% | Not measured | Only GPT-4o-mini measured |
| MMLU Pro (Artificial Analysis run) | 64.8% | Not measured | Only GPT-4o-mini measured |
| MMLU | 81.8% | Not measured | Only GPT-4o-mini measured |
| Neither model has a result on the other 13 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-4o-mini | Qwen3.8 Omni Flash | Leader |
|---|---|---|---|
| IFBench | 31.0% | Not measured | Only GPT-4o-mini measured |
| DTBench | 54.4% | Not measured | Only GPT-4o-mini measured |
| LMCA | 10.4% | Not measured | Only GPT-4o-mini measured |
| PIQA | 88.7% | Not measured | Only GPT-4o-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-4o-mini | Qwen3.8 Omni Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | July 18, 2024 | September 21, 2026 |
| Context window | 128K | 1M |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.47 |
| 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-4o-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-4o-mini or Qwen3.8 Omni Flash?
GPT-4o-mini costs $0.15 per million input tokens and $0.60 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-4o-mini or Qwen3.8 Omni Flash?
Qwen3.8 Omni Flash, with 1M tokens against 128K.
Has any benchmark tested both GPT-4o-mini and Qwen3.8 Omni Flash?
No benchmark has tested both yet. GPT-4o-mini has results on AA Coding Index, LiveCodeBench (Vals AI) and LiveCodeBench (Artificial Analysis run); Qwen3.8 Omni Flash has none yet. Compare them on price and context instead. Data as of October 22, 2026.