GPT OSS 20B vs Kimi K2 Instruct vs Qwen3 30B-A3B Instruct 2507
Too close to call on our weighted score (GPT OSS 20B 61, Qwen3 30B-A3B Instruct 2507 59, Kimi K2 Instruct 50). The right pick depends on what you value most.
OpenAIGPT OSS 20B
61/100- ECI137.8
- Price$0.07 / $0.25
- Context131K
Moonshot AIKimi K2 Instruct
50/100- ECI139.9
- Price$0.57 / $2.30
- Context131K
Alibaba (Qwen)Qwen3 30B-A3B Instruct 2507
59/100- ECI137.3
- Price$0.10 / $0.30
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (GPT OSS 20B 61/100, Qwen3 30B-A3B Instruct 2507 59/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability, GPT OSS 20B on price and Qwen3 30B-A3B Instruct 2507 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityKimi K2 InstructCapabilities Index (ECI): Kimi K2 Instruct 139.9 · GPT OSS 20B 137.8 · Qwen3 30B-A3B Instruct 2507 137.3
- Lowest priceGPT OSS 20BGPT OSS 20B $0.115 · Qwen3 30B-A3B Instruct 2507 $0.15 · Kimi K2 Instruct $1.00 per 1M tokens (3:1 blend)
- Longest contextQwen3 30B-A3B Instruct 2507Qwen3 30B-A3B Instruct 2507 262,144 · GPT OSS 20B 131,072 · Kimi K2 Instruct 131,072 tokens
- Widest inputsSame inputsGPT OSS 20B: Text · Kimi K2 Instruct: Text · Qwen3 30B-A3B Instruct 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GPT OSS 20B | Kimi K2 Instruct | Qwen3 30B-A3B Instruct 2507 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 61 | 64 | 61 |
| Price | 25% | 90 | 48 | 85 |
| Inputs & features | 15% | 35 | 25 | 25 |
| Context window | 10% | 25 | 25 | 37 |
| Overall | 100% | 61/100 | 50/100 | 59/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT OSS 20BOpenAI | Kimi K2 InstructMoonshot AI | Qwen3 30B-A3B Instruct 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.8 | 139.9 (best) | 137.3 |
| ECI rank | #122 of 165 | #112 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 (best) | $0.57 | $0.10 |
| Output | $0.25 (best) | $2.30 | $0.30 |
| Cached input | — | — | — |
| Blended (3:1) | $0.115 (best) | $1.00 | $0.15 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 37 providers | Median of 11 providers | Median of 12 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| 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 | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenModified MIT | OpenApache 2.0 |
| API model ID | — | — | — |
| API providers | 41 (best) | 11 | 14 |
| Released | Aug 5, 2025 | Jul 11, 2025 | Jul 28, 2025 |
| Knowledge cutoff | Jun 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
- Kimi K2 Instruct$10.30
- Qwen3 30B-A3B Instruct 2507$1.60
Which should you choose?
Which is better: GPT OSS 20B, Kimi K2 Instruct or Qwen3 30B-A3B Instruct 2507?
It is close. Our weighted score puts them within 2 points (GPT OSS 20B 61/100, Qwen3 30B-A3B Instruct 2507 59/100, Kimi K2 Instruct 50/100), so choose by what matters most for your work: Kimi K2 Instruct for raw capability, GPT OSS 20B on price and Qwen3 30B-A3B Instruct 2507 for long inputs. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT OSS 20B, Kimi K2 Instruct 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). Qwen3 30B-A3B Instruct 2507 costs $0.10 input / $0.30 output per million tokens (median across 12 API providers); Kimi K2 Instruct costs $0.57 input / $2.30 output per million tokens (median across 11 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 $0.15 for Qwen3 30B-A3B Instruct 2507 (1.3× as much) and $1.00 for Kimi K2 Instruct (8.7× as much).
Which scores higher on benchmarks?
Kimi K2 Instruct scores higher on the Capabilities Index (ECI): Kimi K2 Instruct 139.9 (#112 of 165), GPT OSS 20B 137.8 (#122 of 165) and Qwen3 30B-A3B Instruct 2507 137.3 (#124 of 165). The confidence ranges of the top two overlap (136.3–141.6 vs 133.1–139.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GPT OSS 20B, Kimi K2 Instruct and Qwen3 30B-A3B Instruct 2507 yet, so there is no like-for-like coding score. On overall capability, Kimi K2 Instruct leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3 30B-A3B Instruct 2507 has the largest context window at 262,144 tokens, against 131,072 for GPT OSS 20B and 131,072 for Kimi K2 Instruct. Maximum output per response: GPT OSS 20B up to 32,768, Kimi K2 Instruct 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; Kimi K2 Instruct accepts text; Qwen3 30B-A3B Instruct 2507 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Modified MIT and Apache 2.0), so you can self-host them.
Which is newer?
GPT OSS 20B is the newest, released Aug 5, 2025. Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025; Kimi K2 Instruct came out Jul 11, 2025. Knowledge cutoff: GPT OSS 20B Jun 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.
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