Qwen Turbo vs Aya Expanse 32B vs Gemma 3 12B IT
Too close to call on our weighted score (Qwen Turbo 72, Gemma 3 12B IT 70, Aya Expanse 32B 33). The right pick depends on what you value most.
Alibaba (Qwen)Qwen Turbo
72/100- ECI—
- Price$0.05 / $0.20
- Context1M
CohereAya Expanse 32B
33/100- ECI—
- Price$0.50 / $1.50
- Context128K
GoogleGemma 3 12B IT
70/100- ECI123.5
- Price$0.05 / $0.15
- Context131K
Too close to call
It is close. Our weighted score puts them within 3 points (Qwen Turbo 72/100, Gemma 3 12B IT 70/100, Aya Expanse 32B 33/100), so choose by what matters most for your work: Gemma 3 12B IT on price and Qwen Turbo for long inputs. 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 priceGemma 3 12B ITGemma 3 12B IT $0.075 · Qwen Turbo $0.087 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen TurboQwen Turbo 1,000,000 · Gemma 3 12B IT 131,072 · Aya Expanse 32B 128,000 tokens
- Widest inputsGemma 3 12B ITQwen Turbo: Text · Aya Expanse 32B: Text · Gemma 3 12B IT: Text, Images
- Self-hostingAya Expanse 32B and Gemma 3 12B ITPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | Qwen Turbo | Aya Expanse 32B | Gemma 3 12B IT |
|---|---|---|---|---|
| Price | 50% | 100 | 56 | 100 |
| Inputs & features | 30% | 35 | 0 | 50 |
| Context window | 20% | 60 | 24 | 24 |
| Overall | 100% | 72/100 | 33/100 | 70/100 |
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 | Qwen TurboAlibaba (Qwen) | Aya Expanse 32BCohere | Gemma 3 12B ITGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 123.5 |
| ECI rank | — | — | #138 of 148 |
| GPQA DiamondGraduate-level science questions | 41.8% (best) | — | 39.5% |
| OTIS Mock AIME 2024–2025Competition mathematics | 6.1% | — | 16.7% (best) |
| Price per million tokens | |||
| Input | $0.05 (best) | $0.50 | $0.05 (best) |
| Output | $0.20 | $1.50 | $0.15 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.087 | $0.75 | $0.075 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Median of 1 providers | Median of 7 providers |
| Limits | |||
| Context window | 1,000,000 tokens (best) | 128,000 tokens | 131,072 tokens |
| Max output | 16,384 tokens | 4,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | OpenCC-BY-NC-4.0 | Open |
| API model ID | qwen-turbo | c4ai-aya-expanse-32b | — |
| API providers | 3 | 2 | 7 (best) |
| Released | Nov 1, 2024 | Oct 24, 2024 | Mar 12, 2025 |
| Knowledge cutoff | Apr 2024 | — | Aug 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen Turbo$0.90
- Aya Expanse 32B$8.00
- Gemma 3 12B IT$0.80
Which should you choose?
Which is better: Qwen Turbo, Aya Expanse 32B or Gemma 3 12B IT?
It is close. Our weighted score puts them within 3 points (Qwen Turbo 72/100, Gemma 3 12B IT 70/100, Aya Expanse 32B 33/100), so choose by what matters most for your work: Gemma 3 12B IT on price and Qwen Turbo for long inputs. 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, Qwen Turbo, Aya Expanse 32B or Gemma 3 12B IT?
Gemma 3 12B IT is cheaper at $0.05 input / $0.15 output per million tokens (median across 7 API providers). Qwen Turbo costs $0.05 input / $0.20 output per million tokens (official Alibaba API price); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.075 per million tokens for Gemma 3 12B IT versus $0.087 for Qwen Turbo (1.2× as much) and $0.75 for Aya Expanse 32B (10× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen Turbo has not been scored yet, Aya Expanse 32B has not been scored yet and Gemma 3 12B IT has an ECI of 123.5.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen Turbo, Aya Expanse 32B and Gemma 3 12B IT yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen Turbo has the largest context window at 1,000,000 tokens, against 131,072 for Gemma 3 12B IT and 128,000 for Aya Expanse 32B. Maximum output per response: Qwen Turbo up to 16,384, Aya Expanse 32B up to 4,000, Gemma 3 12B IT up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen Turbo accepts text; Aya Expanse 32B accepts text; Gemma 3 12B IT accepts text and images. Gemma 3 12B IT handles the widest range of inputs.
Are any of these open source?
Aya Expanse 32B and Gemma 3 12B IT publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen Turbo is proprietary.
Which is newer?
Gemma 3 12B IT is the newest, released Mar 12, 2025. Qwen Turbo came out Nov 1, 2024; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: Qwen Turbo Apr 2024, Gemma 3 12B IT Aug 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 Oct 4, 2026.
