GPT-5 Image vs Qwen3.8 Flash
Qwen3.8 Flash costs 21.3x less per output token than GPT-5 Image ($0.47 vs $10.00 per million). Updated October 21, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $10.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.47
GPT-5 Image or Qwen3.8 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 Image has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. 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
Image generation
Design Arena: images
No call
Only GPT-5 Image has a Design Arena: images result, 1191.
Directional only
Long documents
Listed context window
Qwen3.8 Flash
Qwen3.8 Flash takes 1M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 Flash
GPT-5 Image $0.015, Qwen3.8 Flash $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-5 Image not benchmarked, Qwen3.8 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 Image $10.00 against Qwen3.8 Flash $0.47 per million output tokens, 21.3x apart.
- Context window
- GPT-5 Image takes 400K tokens, Qwen3.8 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 Index1 board measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 37 boards here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench research | Not measured | 74.6% | Only Qwen3.8 Flash measured |
| LiveBench browser | Not measured | 48.0% | Only Qwen3.8 Flash measured |
| LiveBench long agentic | Not measured | 37.1% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 36 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 87.4% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 20 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench design | Not measured | 72.8% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 8 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 74.2% | Only Qwen3.8 Flash measured |
| Neither model has a result on the other 45 boards here | |||
Image generationDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-5 Image | Qwen3.8 Flash | Leader |
|---|---|---|---|
| Design Arena: images | 1191 | Not measured | Only GPT-5 Image measured |
| Design Arena: graphic design | 1172 | Not measured | Only GPT-5 Image measured |
| Design Arena: logos | 1202 | Not measured | Only GPT-5 Image 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
Qwen3.8 Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.8 Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 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 Image | Qwen3.8 Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | October 14, 2025 | August 26, 2026 |
| Context window | 400K | 1M |
| Input $/Mtok | $10.00 | $0.15 |
| Output $/Mtok | $10.00 | $0.47 |
| 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 GPT-5 Image better than Qwen3.8 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 Image or Qwen3.8 Flash?
GPT-5 Image costs $10.00 per million input tokens and $10.00 per million output tokens; Qwen3.8 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 Image or Qwen3.8 Flash?
Qwen3.8 Flash, with 1M tokens against 400K.
Has any benchmark tested both GPT-5 Image and Qwen3.8 Flash?
No benchmark has tested both yet. GPT-5 Image has results on Design Arena: images, Design Arena: graphic design and Design Arena: logos; Qwen3.8 Flash on LiveBench coding, LiveBench research and LiveBench browser. Compare them on price and context instead. Data as of October 21, 2026.