Qwen3 30B-A3B Instruct 2507 vs ERNIE 4.5 VL 424B A47B vs GPT OSS 20B
GPT OSS 20B comes out ahead, 61 to 57 and 44 on our weighted score, and it is the cheaper option too.
Alibaba (Qwen)Qwen3 30B-A3B Instruct 2507
57/100- ECI137.3
- Price$0.10 / $0.30
- Context262K
BaiduERNIE 4.5 VL 424B A47B
44/100- ECI—
- Price$0.42 / $1.25
- Context123K
- Our pick
OpenAIGPT OSS 20B
61/100- ECI137.8
- Price$0.07 / $0.25
- Context131K
GPT OSS 20B is our pick
GPT OSS 20B is the better all-round choice, scoring 61/100 against Qwen3 30B-A3B Instruct 2507 (57) and ERNIE 4.5 VL 424B A47B (44). It leads on price. Qwen3 30B-A3B Instruct 2507 wins on context window. 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 · Qwen3 30B-A3B Instruct 2507 $0.15 · ERNIE 4.5 VL 424B A47B $0.627 per 1M tokens (3:1 blend)
- Longest contextQwen3 30B-A3B Instruct 2507Qwen3 30B-A3B Instruct 2507 262,144 · GPT OSS 20B 131,072 · ERNIE 4.5 VL 424B A47B 123,000 tokens
- Widest inputsERNIE 4.5 VL 424B A47B Qwen3 30B-A3B Instruct 2507: Text · ERNIE 4.5 VL 424B A47B : Text, Images · GPT OSS 20B: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3 30B-A3B Instruct 2507 | ERNIE 4.5 VL 424B A47B | GPT OSS 20B |
|---|---|---|---|---|
| Price | 50% | 85 | 57 | 90 |
| Inputs & features | 30% | 25 | 35 | 35 |
| Context window | 20% | 37 | 23 | 25 |
| Overall | 100% | 57/100 | 44/100 | 61/100 |
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 | Qwen3 30B-A3B Instruct 2507Alibaba (Qwen) | ERNIE 4.5 VL 424B A47B Baidu | GPT OSS 20BOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 137.3 | — | 137.8 (best) |
| ECI rank | #124 of 165 | — | #122 of 165 (best) |
| GPQA DiamondGraduate-level science questions | 55.6% | — | 60.8% (best) |
| OTIS Mock AIME 2024–2025Competition mathematics | 62.2% | — | 65.3% (best) |
| Price per million tokens | |||
| Input | $0.10 | $0.42 | $0.07 (best) |
| Output | $0.30 | $1.25 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.15 | $0.627 | $0.115 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 12 providers | Median of 1 providers | Median of 37 providers |
| Limits | |||
| Context window | 262,144 tokens (best) | 123,000 tokens | 131,072 tokens |
| Max output | 16,384 tokens | 16,000 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | Yes | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | OpenApache 2.0 | Open | Open |
| API model ID | — | — | — |
| API providers | 14 | 1 | 41 (best) |
| Released | Jul 28, 2025 | Jun 30, 2025 | Aug 5, 2025 |
| Knowledge cutoff | — | Mar 31, 2025 | 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.
- Qwen3 30B-A3B Instruct 2507$1.60
- ERNIE 4.5 VL 424B A47B $6.70
- GPT OSS 20B$1.20
Which should you choose?
Which is better: Qwen3 30B-A3B Instruct 2507, ERNIE 4.5 VL 424B A47B or GPT OSS 20B?
GPT OSS 20B is the better all-round choice, scoring 61/100 against Qwen3 30B-A3B Instruct 2507 (57) and ERNIE 4.5 VL 424B A47B (44). It leads on price. Qwen3 30B-A3B Instruct 2507 wins on context window. 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, Qwen3 30B-A3B Instruct 2507, ERNIE 4.5 VL 424B A47B or GPT OSS 20B?
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); ERNIE 4.5 VL 424B A47B costs $0.42 input / $1.25 output per million tokens (median across 1 API provider). 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 $0.627 for ERNIE 4.5 VL 424B A47B (5.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3 30B-A3B Instruct 2507 has an ECI of 137.3, ERNIE 4.5 VL 424B A47B has not been scored yet and GPT OSS 20B has an ECI of 137.8.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3 30B-A3B Instruct 2507, ERNIE 4.5 VL 424B A47B and GPT OSS 20B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that ERNIE 4.5 VL 424B A47B does not support tool calling, which most coding agents need.
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 123,000 for ERNIE 4.5 VL 424B A47B . Maximum output per response: Qwen3 30B-A3B Instruct 2507 up to 16,384, ERNIE 4.5 VL 424B A47B up to 16,000, GPT OSS 20B up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Qwen3 30B-A3B Instruct 2507 accepts text; ERNIE 4.5 VL 424B A47B accepts text and images; GPT OSS 20B accepts text. ERNIE 4.5 VL 424B A47B handles the widest range of inputs.
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 20B is the newest, released Aug 5, 2025. Qwen3 30B-A3B Instruct 2507 came out Jul 28, 2025; ERNIE 4.5 VL 424B A47B came out Jun 30, 2025. Knowledge cutoff: ERNIE 4.5 VL 424B A47B Mar 31, 2025, 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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