GPT-5 Image Mini vs Qwen3.5-9B
Qwen3.5-9B costs 13.3x less per output token than GPT-5 Image Mini ($0.15 vs $2.00 per million). Updated October 21, 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
- $0.15
GPT-5 Image Mini or Qwen3.5-9B: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-5 Image Mini for a larger context window (400K against 262K tokens).
No benchmark has tested both yet. GPT-5 Image Mini has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; Qwen3.5-9B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run). 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 Qwen3.5-9B has a AA Intelligence Index result, 13.3.
Directional only
Image generation
Design Arena: images
No call
Only GPT-5 Image Mini has a Design Arena: images result, 1184.
Directional only
Long documents
Listed context window
GPT-5 Image Mini
GPT-5 Image Mini takes 400K tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-9B
GPT-5 Image Mini $0.0035, Qwen3.5-9B $0.0002 per turn.
List prices
At a glance
- Coding index
- GPT-5 Image Mini not benchmarked, Qwen3.5-9B 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 Image Mini $2.00 against Qwen3.5-9B $0.15 per million output tokens, 13.3x apart.
- Context window
- GPT-5 Image Mini takes 400K tokens, Qwen3.5-9B 262K.
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-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Coding Index | Not measured | 23.5 | Only Qwen3.5-9B measured |
| SciCode | Not measured | 29.5% | Only Qwen3.5-9B measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 24.2% | Only Qwen3.5-9B measured |
| Terminal-Bench | Not measured | 9.2% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 13.3 | Only Qwen3.5-9B measured |
| Epoch Capabilities Index | Not measured | 139.3 | Only Qwen3.5-9B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| τ²-Bench Telecom | Not measured | 86.8% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 40 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 80.6% | Only Qwen3.5-9B measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 14.9% | Only Qwen3.5-9B measured |
| GPQA Diamond (Epoch AI run) | Not measured | 79.0% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 19 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 61.7% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 12 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| IFBench | Not measured | 66.7% | Only Qwen3.5-9B measured |
| Long context reasoning (AA-LCR) | Not measured | 70.0% | Only Qwen3.5-9B measured |
| DTBench | Not measured | 71.2% | Only Qwen3.5-9B measured |
| LMCA | Not measured | 24.5% | Only Qwen3.5-9B measured |
| Neither model has a result on the other 42 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5 Image Mini | Qwen3.5-9B | Leader |
|---|---|---|---|
| Design Arena: images | 1184 | Not measured | Only GPT-5 Image Mini measured |
| Design Arena: graphic design | 1165 | Not measured | Only GPT-5 Image Mini measured |
| Design Arena: logos | 1209 | Not measured | Only GPT-5 Image Mini measured |
| Neither model has a result on the other 2 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
Qwen3.5-9B costs less
Repository review
50K input + 3K output tokens
Qwen3.5-9B costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-9B 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 Image Mini | Qwen3.5-9B |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 16, 2025 | March 10, 2026 |
| Context window | 400K | 262K |
| Input $/Mtok | $2.50 | $0.10 |
| Output $/Mtok | $2.00 | $0.15 |
| 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-5 Image Mini better than Qwen3.5-9B for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-5 Image Mini for a larger context window (400K against 262K tokens).
Which is cheaper, GPT-5 Image Mini or Qwen3.5-9B?
GPT-5 Image Mini costs $2.50 per million input tokens and $2.00 per million output tokens; Qwen3.5-9B costs $0.10 and $0.15. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-5 Image Mini or Qwen3.5-9B?
GPT-5 Image Mini, with 400K tokens against 262K.
Has any benchmark tested both GPT-5 Image Mini and Qwen3.5-9B?
No benchmark has tested both yet. GPT-5 Image Mini has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; Qwen3.5-9B on AA Coding Index, SciCode and Terminal-Bench Hard (Artificial Analysis run). Compare them on price and context instead. Data as of October 21, 2026.