Qwen3.5 9B Base vs Mistral Large 3 vs Mercury 2
Too close to call on our weighted score (Mistral Large 3 52, Mercury 2 52, Qwen3.5 9B Base 50). The right pick depends on what you value most.
Alibaba (Qwen)Qwen3.5 9B Base
50/100- ECI—
- Price$0.30 / $0.807
- Context262K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
InceptionMercury 2
52/100- ECI—
- Price$0.25 / $0.75
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Mistral Large 3 52/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 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 priceMercury 2Mercury 2 $0.375 · Qwen3.5 9B Base $0.427 · Mistral Large 3 $0.75 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 9B Base and Mistral Large 3Qwen3.5 9B Base 262,144 · Mistral Large 3 262,144 · Mercury 2 128,000 tokens
- Widest inputsQwen3.5 9B BaseQwen3.5 9B Base: Text, Images, Video · Mistral Large 3: Text, Images · Mercury 2: Text
- Self-hostingQwen3.5 9B Base and Mistral Large 3Publishes downloadable weights (Apache-2.0)
| Measure | Weight | Qwen3.5 9B Base | Mistral Large 3 | Mercury 2 |
|---|---|---|---|---|
| Price | 50% | 65 | 53 | 67 |
| Inputs & features | 30% | 35 | 60 | 45 |
| Context window | 20% | 37 | 37 | 24 |
| Overall | 100% | 50/100 | 52/100 | 52/100 |
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 | Qwen3.5 9B BaseAlibaba (Qwen) | Mistral Large 3Mistral AI | Mercury 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.50 | $0.25 (best) |
| Output | $0.807 | $1.50 | $0.75 (best) |
| Cached input | — | $0.05 | $0.025 (best) |
| Blended (3:1) | $0.427 | $0.75 | $0.375 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Mistral API | Official Inception API |
| Limits | |||
| Context window | 262,144 tokens (best) | 262,144 tokens (best) | 128,000 tokens |
| Max output | 65,536 tokens | 262,144 tokens (best) | 50,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | No | No | Yeslow · medium · high |
| Tool calling | No | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | Open | Proprietary |
| API model ID | — | mistral-large-2512 | mercury-2 |
| API providers | 1 | 17 (best) | 7 |
| Released | Feb 26, 2026 | Dec 2, 2025 | Feb 24, 2026 |
| Knowledge cutoff | — | Nov 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Qwen3.5 9B Base$4.61
- Mistral Large 3$8.00
- Mercury 2$4.00
Which should you choose?
Which is better: Qwen3.5 9B Base, Mistral Large 3 or Mercury 2?
It is close. Our weighted score puts them within a point (Mistral Large 3 52/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 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, Qwen3.5 9B Base, Mistral Large 3 or Mercury 2?
Mercury 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). Qwen3.5 9B Base costs $0.30 input / $0.807 output per million tokens (median across 1 API provider); Mistral Large 3 costs $0.50 input / $1.50 output per million tokens (official Mistral API price). At a typical mix of three input tokens to one output token, that is $0.375 per million tokens for Mercury 2 versus $0.427 for Qwen3.5 9B Base (1.1× as much) and $0.75 for Mistral Large 3 (2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.5 9B Base has not been scored yet, Mistral Large 3 has not been scored yet and Mercury 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 9B Base, Mistral Large 3 and Mercury 2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen3.5 9B Base does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3.5 9B Base and Mistral Large 3 have the largest context windows (262,144 and 262,144 tokens), against 128,000 for Mercury 2. Maximum output per response: Qwen3.5 9B Base up to 65,536, Mistral Large 3 up to 262,144, Mercury 2 up to 50,000 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 9B Base accepts text, images and video; Mistral Large 3 accepts text and images; Mercury 2 accepts text. Qwen3.5 9B Base handles the widest range of inputs.
Are any of these open source?
Qwen3.5 9B Base and Mistral Large 3 publishes its weights (Apache-2.0) and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Qwen3.5 9B Base is the newest, released Feb 26, 2026. Mercury 2 came out Feb 24, 2026; Mistral Large 3 came out Dec 2, 2025. Knowledge cutoff: Mistral Large 3 Nov 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.
