GPT OSS 120B vs Qwen3 14B vs Qwen3 235B-A22B Instruct 2507
GPT OSS 120B comes out ahead, 61 to 58 and 54 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT OSS 120B
61/100- ECI140.0
- Price$0.15 / $0.60
- Context131K
Alibaba (Qwen)Qwen3 14B
54/100- ECI138.2
- Price$0.35 / $1.40
- Context131K
- Alibaba (Qwen)
Qwen3 235B-A22B Instruct 2507
58/100- ECI138.9
- Price$0.15 / $0.75
- Context262K
GPT OSS 120B is our pick
GPT OSS 120B is the better all-round choice, scoring 61/100 against Qwen3 235B-A22B Instruct 2507 (58) and Qwen3 14B (54). It leads on price and inputs & features. Qwen3 235B-A22B Instruct 2507 wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityGPT OSS 120BCapabilities Index (ECI): GPT OSS 120B 140.0 · Qwen3 235B-A22B Instruct 2507 138.9 · Qwen3 14B 138.2
- Lowest priceGPT OSS 120BGPT OSS 120B $0.263 · Qwen3 235B-A22B Instruct 2507 $0.30 · Qwen3 14B $0.613 per 1M tokens (3:1 blend)
- Longest contextQwen3 235B-A22B Instruct 2507Qwen3 235B-A22B Instruct 2507 262,144 · GPT OSS 120B 131,072 · Qwen3 14B 131,072 tokens
- Widest inputsSame inputsGPT OSS 120B: Text · Qwen3 14B: Text · Qwen3 235B-A22B Instruct 2507: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GPT OSS 120B | Qwen3 14B | Qwen3 235B-A22B Instruct 2507 |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 65 | 63 | 64 |
| Price | 25% | 77 | 60 | 75 |
| Inputs & features | 15% | 45 | 35 | 25 |
| Context window | 10% | 24 | 24 | 37 |
| Overall | 100% | 61/100 | 54/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | GPT OSS 120BOpenAI | Qwen3 14BAlibaba (Qwen) | Qwen3 235B-A22B Instruct 2507Alibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 140.0 (best) | 138.2 | 138.9 |
| ECI rank | #99 of 148 (best) | #107 of 148 | #105 of 148 |
| GPQA DiamondGraduate-level science questions | 75.8% (best) | 63.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 88.9% (best) | 66.4% | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.35 | $0.15 (best) |
| Output | $0.60 (best) | $1.40 | $0.75 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.613 | $0.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 36 providers | Official Alibaba API | Median of 11 providers |
| Limits | |||
| Context window | 131,072 tokens | 131,072 tokens | 262,144 tokens (best) |
| Max output | 32,768 tokens (best) | 8,192 tokens | 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 | Yes | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | — | qwen3-14b | — |
| API providers | 39 (best) | 1 | 11 |
| Released | Aug 5, 2025 | Apr 29, 2025 | Jul 21, 2025 |
| Knowledge cutoff | — | Apr 2025 | — |
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 120B$2.70
- Qwen3 14B$6.30
- Qwen3 235B-A22B Instruct 2507$3.00
Which should you choose?
Which is better: GPT OSS 120B, Qwen3 14B or Qwen3 235B-A22B Instruct 2507?
GPT OSS 120B is the better all-round choice, scoring 61/100 against Qwen3 235B-A22B Instruct 2507 (58) and Qwen3 14B (54). It leads on price and inputs & features. Qwen3 235B-A22B Instruct 2507 wins on context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, GPT OSS 120B, Qwen3 14B or Qwen3 235B-A22B Instruct 2507?
GPT OSS 120B is cheaper at $0.15 input / $0.60 output per million tokens (median across 36 API providers). Qwen3 235B-A22B Instruct 2507 costs $0.15 input / $0.75 output per million tokens (median across 11 API providers); Qwen3 14B costs $0.35 input / $1.40 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for GPT OSS 120B versus $0.30 for Qwen3 235B-A22B Instruct 2507 (1.1× as much) and $0.613 for Qwen3 14B (2.3× as much).
Which scores higher on benchmarks?
GPT OSS 120B scores higher on the Capabilities Index (ECI): GPT OSS 120B 140.0 (#99 of 148), Qwen3 235B-A22B Instruct 2507 138.9 (#105 of 148) and Qwen3 14B 138.2 (#107 of 148). The confidence ranges of the top two overlap (135.3–142.3 vs 135.8–140.6), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for GPT OSS 120B, Qwen3 14B and Qwen3 235B-A22B Instruct 2507 yet, so there is no like-for-like coding score. On overall capability, GPT OSS 120B 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 235B-A22B Instruct 2507 has the largest context window at 262,144 tokens, against 131,072 for GPT OSS 120B and 131,072 for Qwen3 14B. Maximum output per response: GPT OSS 120B up to 32,768, Qwen3 14B up to 8,192, Qwen3 235B-A22B Instruct 2507 up to 16,384 tokens.
Which can read images, PDFs, audio or video?
GPT OSS 120B accepts text; Qwen3 14B accepts text; Qwen3 235B-A22B Instruct 2507 accepts text. They handle the same number of input types.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
GPT OSS 120B is the newest, released Aug 5, 2025. Qwen3 235B-A22B Instruct 2507 came out Jul 21, 2025; Qwen3 14B came out Apr 29, 2025. Knowledge cutoff: Qwen3 14B Apr 2025.
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 Oct 4, 2026.
