GPT OSS 20B vs Qwen Flash vs Qwen3 30B-A3B Instruct 2507
Qwen Flash comes out ahead, 69 to 61 and 57 on our weighted score, though GPT OSS 20B is 16% cheaper per token.
OpenAIGPT OSS 20B
61/100- ECI137.8
- Price$0.07 / $0.25
- Context131K
- Our pick
Alibaba (Qwen)Qwen Flash
69/100- ECI—
- Price$0.05 / $0.40
- Context1M
- Alibaba (Qwen)
Qwen3 30B-A3B Instruct 2507
57/100- ECI137.3
- Price$0.10 / $0.30
- Context262K
Qwen Flash is our pick
Qwen Flash is the better all-round choice, scoring 69/100 against GPT OSS 20B (61) and Qwen3 30B-A3B Instruct 2507 (57). It leads on inputs & features and context window. GPT OSS 20B wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
- CapabilityNot enough dataNo independent benchmark covers every model here yet
- Lowest priceGPT OSS 20BGPT OSS 20B $0.115 · Qwen Flash $0.138 · Qwen3 30B-A3B Instruct 2507 $0.15 per 1M tokens (3:1 blend)
- Longest contextQwen FlashQwen Flash 1,000,000 · Qwen3 30B-A3B Instruct 2507 262,144 · GPT OSS 20B 131,072 tokens
- Widest inputsSame inputsGPT OSS 20B: Text · Qwen Flash: Text · Qwen3 30B-A3B Instruct 2507: Text
- Self-hostingGPT OSS 20B and Qwen3 30B-A3B Instruct 2507Publishes downloadable weights (Apache 2.0)
| Measure | Weight | GPT OSS 20B | Qwen Flash | Qwen3 30B-A3B Instruct 2507 |
|---|---|---|---|---|
| Price | 50% | 90 | 87 | 85 |
| Inputs & features | 30% | 35 | 45 | 25 |
| Context window | 20% | 25 | 60 | 37 |
| Overall | 100% | 61/100 | 69/100 | 57/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT OSS 20BOpenAI | Qwen FlashAlibaba (Qwen) | Qwen3 30B-A3B Instruct 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.8 (best) | — | 137.3 |
| ECI rank | #122 of 165 (best) | — | #124 of 165 |
| GPQA DiamondGraduate-level science questions | 60.8% (best) | — | 55.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 65.3% (best) | — | 62.2% |
| Price per million tokens | |||
| Input | $0.07 | $0.05 (best) | $0.10 |
| Output | $0.25 (best) | $0.40 | $0.30 |
| Cached input | — | $0.01 | — |
| Blended (3:1) | $0.115 (best) | $0.138 | $0.15 |
| Long-context rate | Same rate | Over 256K: $0.25 / $2.00 | Same rate |
| Price source | Median of 37 providers | Official Alibaba API | Median of 12 providers |
| Limits | |||
| Context window | 131,072 tokens | 1,000,000 tokens (best) | 262,144 tokens |
| Max output | 32,768 tokens (best) | 32,768 tokens (best) | 16,384 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | OpenApache 2.0 |
| API model ID | — | qwen-flash | — |
| API providers | 41 (best) | 6 | 14 |
| Released | Aug 5, 2025 | Jul 28, 2025 | Jul 28, 2025 |
| Knowledge cutoff | Jun 2024 | Apr 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT OSS 20B$1.20
- Qwen Flash$1.30
- Qwen3 30B-A3B Instruct 2507$1.60
Which should you choose?
Which is better: GPT OSS 20B, Qwen Flash or Qwen3 30B-A3B Instruct 2507?
Qwen Flash is the better all-round choice, scoring 69/100 against GPT OSS 20B (61) and Qwen3 30B-A3B Instruct 2507 (57). It leads on inputs & features and context window. GPT OSS 20B wins on price. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, GPT OSS 20B, Qwen Flash or Qwen3 30B-A3B Instruct 2507?
GPT OSS 20B is cheaper at $0.07 input / $0.25 output per million tokens (median across 37 API providers). Qwen Flash costs $0.05 input / $0.40 output per million tokens (official Alibaba API price); Qwen3 30B-A3B Instruct 2507 costs $0.10 input / $0.30 output per million tokens (median across 12 API providers). At a typical mix of three input tokens to one output token, that is $0.115 per million tokens for GPT OSS 20B versus $0.138 for Qwen Flash (1.2× as much) and $0.15 for Qwen3 30B-A3B Instruct 2507 (1.3× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT OSS 20B has an ECI of 137.8, Qwen Flash has not been scored yet and Qwen3 30B-A3B Instruct 2507 has an ECI of 137.3.
Which is better for coding?
There are no published SWE-bench Verified results for GPT OSS 20B, Qwen Flash and Qwen3 30B-A3B Instruct 2507 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen Flash has the largest context window at 1,000,000 tokens, against 262,144 for Qwen3 30B-A3B Instruct 2507 and 131,072 for GPT OSS 20B. Maximum output per response: GPT OSS 20B up to 32,768, Qwen Flash up to 32,768, Qwen3 30B-A3B Instruct 2507 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GPT OSS 20B accepts text; Qwen Flash accepts text; Qwen3 30B-A3B Instruct 2507 accepts text. They handle the same number of input types.
Are any of these open source?
GPT OSS 20B and Qwen3 30B-A3B Instruct 2507 publishes its weights (Apache 2.0) and can be self-hosted; Qwen Flash is proprietary.
Which is newer?
GPT OSS 20B is the newest, released Aug 5, 2025. Qwen Flash came out Jul 28, 2025; Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025. Knowledge cutoff: GPT OSS 20B Jun 2024, Qwen Flash Apr 2024.
How do you decide the winner?
Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.
