Qwen3.5 9B Base vs Trinity Large Thinking vs Mercury 2
Too close to call on our weighted score (Trinity Large Thinking 53, 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
Arcee AITrinity Large Thinking
53/100- ECI—
- Price$0.25 / $0.80
- Context524K
InceptionMercury 2
52/100- ECI—
- Price$0.25 / $0.75
- Context128K
Too close to call
It is close. Our weighted score puts them within 2 points (Trinity Large Thinking 53/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 on price and Trinity Large Thinking 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 priceMercury 2Mercury 2 $0.375 · Trinity Large Thinking $0.388 · Qwen3.5 9B Base $0.427 per 1M tokens (3:1 blend)
- Longest contextTrinity Large ThinkingTrinity Large Thinking 524,288 · Qwen3.5 9B Base 262,144 · Mercury 2 128,000 tokens
- Widest inputsQwen3.5 9B BaseQwen3.5 9B Base: Text, Images, Video · Trinity Large Thinking: Text · Mercury 2: Text
- Self-hostingQwen3.5 9B Base and Trinity Large ThinkingPublishes downloadable weights (Apache-2.0 and OpenMDW-1.1)
| Measure | Weight | Qwen3.5 9B Base | Trinity Large Thinking | Mercury 2 |
|---|---|---|---|---|
| Price | 50% | 65 | 66 | 67 |
| Inputs & features | 30% | 35 | 35 | 45 |
| Context window | 20% | 37 | 49 | 24 |
| Overall | 100% | 50/100 | 53/100 | 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) | Trinity Large ThinkingArcee AI | Mercury 2Inception |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.30 | $0.25 (best) | $0.25 (best) |
| Output | $0.807 | $0.80 | $0.75 (best) |
| Cached input | — | $0.06 | $0.025 (best) |
| Blended (3:1) | $0.427 | $0.388 | $0.375 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Official Arcee API | Official Inception API |
| Limits | |||
| Context window | 262,144 tokens | 524,288 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 | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | Yes | No | No |
| Reasoning | No | Yes | Yeslow · medium · high |
| Tool calling | No | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | OpenApache-2.0 | OpenOpenMDW-1.1 | Proprietary |
| API model ID | — | trinity-large-thinking | mercury-2 |
| API providers | 1 | 3 | 7 (best) |
| Released | Feb 26, 2026 | Apr 1, 2026 | Feb 24, 2026 |
| Knowledge cutoff | — | — | — |
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
- Trinity Large Thinking$4.10
- Mercury 2$4.00
Which should you choose?
Which is better: Qwen3.5 9B Base, Trinity Large Thinking or Mercury 2?
It is close. Our weighted score puts them within 2 points (Trinity Large Thinking 53/100, Mercury 2 52/100, Qwen3.5 9B Base 50/100), so choose by what matters most for your work: Mercury 2 on price and Trinity Large Thinking 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, Qwen3.5 9B Base, Trinity Large Thinking or Mercury 2?
Mercury 2 is cheaper at $0.25 input / $0.75 output per million tokens (official Inception API price). Trinity Large Thinking costs $0.25 input / $0.80 output per million tokens (official Arcee API price); Qwen3.5 9B Base costs $0.30 input / $0.807 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.375 per million tokens for Mercury 2 versus $0.388 for Trinity Large Thinking (1× as much) and $0.427 for Qwen3.5 9B Base (1.1× 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, Trinity Large Thinking 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, Trinity Large Thinking 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?
Trinity Large Thinking has the largest context window at 524,288 tokens, against 262,144 for Qwen3.5 9B Base and 128,000 for Mercury 2. Maximum output per response: Qwen3.5 9B Base up to 65,536, Trinity Large Thinking 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; Trinity Large Thinking accepts text; 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 Trinity Large Thinking publishes its weights (Apache-2.0 and OpenMDW-1.1) and can be self-hosted; Mercury 2 is proprietary.
Which is newer?
Trinity Large Thinking is the newest, released Apr 1, 2026. Qwen3.5 9B Base came out Feb 26, 2026; Mercury 2 came out Feb 24, 2026.
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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