Mercury 2 vs Mistral Large 3 vs Qwen3.5 35B-A3B Base
Too close to call on our weighted score (Mistral Large 3 52, Mercury 2 52, Qwen3.5 35B-A3B Base 45). The right pick depends on what you value most.
InceptionMercury 2
52/100- ECI—
- Price$0.25 / $0.75
- Context128K
Mistral AIMistral Large 3
52/100- ECI—
- Price$0.50 / $1.50
- Context262K
Alibaba (Qwen)Qwen3.5 35B-A3B Base
45/100- ECI—
- Price$0.54 / $1.33
- Context262K
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 35B-A3B Base 45/100), 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 35B-A3B Base $0.739 · Mistral Large 3 $0.75 per 1M tokens (3:1 blend)
- Longest contextMistral Large 3 and Qwen3.5 35B-A3B BaseMistral Large 3 262,144 · Qwen3.5 35B-A3B Base 262,144 · Mercury 2 128,000 tokens
- Widest inputsQwen3.5 35B-A3B BaseMercury 2: Text · Mistral Large 3: Text, Images · Qwen3.5 35B-A3B Base: Text, Images, Video
- Self-hostingMistral Large 3 and Qwen3.5 35B-A3B BasePublishes downloadable weights (Apache-2.0)
| Measure | Weight | Mercury 2 | Mistral Large 3 | Qwen3.5 35B-A3B Base |
|---|---|---|---|---|
| Price | 50% | 67 | 53 | 54 |
| Inputs & features | 30% | 45 | 60 | 35 |
| Context window | 20% | 24 | 37 | 37 |
| Overall | 100% | 52/100 | 52/100 | 45/100 |
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 | Mercury 2Inception | Mistral Large 3Mistral AI | Qwen3.5 35B-A3B BaseAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.25 (best) | $0.50 | $0.54 |
| Output | $0.75 (best) | $1.50 | $1.33 |
| Cached input | $0.025 (best) | $0.05 | — |
| Blended (3:1) | $0.375 (best) | $0.75 | $0.739 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Inception API | Official Mistral API | Median of 1 providers |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 50,000 tokens | 262,144 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Open | OpenApache-2.0 |
| API model ID | mercury-2 | mistral-large-2512 | — |
| API providers | 7 | 17 (best) | 1 |
| Released | Feb 24, 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.
- Mercury 2$4.00
- Mistral Large 3$8.00
- Qwen3.5 35B-A3B Base$8.07
Which should you choose?
Which is better: Mercury 2, Mistral Large 3 or Qwen3.5 35B-A3B Base?
It is close. Our weighted score puts them within a point (Mistral Large 3 52/100, Mercury 2 52/100, Qwen3.5 35B-A3B Base 45/100), 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, Mercury 2, Mistral Large 3 or Qwen3.5 35B-A3B Base?
Mercury 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). Qwen3.5 35B-A3B Base costs $0.54 input / $1.33 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 $0.739 for Qwen3.5 35B-A3B Base (2× 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. Mercury 2 has not been scored yet, Mistral Large 3 has not been scored yet and Qwen3.5 35B-A3B Base has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Mercury 2, Mistral Large 3 and Qwen3.5 35B-A3B Base yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen3.5 35B-A3B Base does not support tool calling, which most coding agents need.
Which has the bigger context window?
Mistral Large 3 and Qwen3.5 35B-A3B Base have the largest context windows (262,144 and 262,144 tokens), against 128,000 for Mercury 2. Maximum output per response: Mercury 2 up to 50,000, Mistral Large 3 up to 262,144, Qwen3.5 35B-A3B Base up to 65,536 tokens.
Which can read images, PDFs, audio or video?
Mercury 2 accepts text; Mistral Large 3 accepts text and images; Qwen3.5 35B-A3B Base accepts text, images and video. Qwen3.5 35B-A3B Base handles the widest range of inputs.
Are any of these open source?
Mistral Large 3 and Qwen3.5 35B-A3B Base publishes its weights (Apache-2.0) and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Mercury 2 is the newest, released Feb 24, 2026. Qwen3.5 35B-A3B Base 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.
