MiMo-V2-Pro vs Qwen3 VL 235B A22B Thinking vs Relace Search
Too close to call on our weighted score (Qwen3 VL 235B A22B Thinking 44, MiMo-V2-Pro 42, Relace Search 35). The right pick depends on what you value most.
XiaomiMiMo-V2-Pro
42/100- ECI—
- Price$1.00 / $3.00
- Context1.05M
Alibaba (Qwen)Qwen3 VL 235B A22B Thinking
44/100- ECI—
- Price$0.50 / $3.95
- Context131K
Relace
Relace Search
35/100- ECI—
- Price$1.00 / $3.00
- Context256K
Too close to call
It is close. Our weighted score puts them within 1 points (Qwen3 VL 235B A22B Thinking 44/100, MiMo-V2-Pro 42/100, Relace Search 35/100), so choose by what matters most for your work: Qwen3 VL 235B A22B Thinking on price and MiMo-V2-Pro 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 priceQwen3 VL 235B A22B ThinkingQwen3 VL 235B A22B Thinking $1.36 · MiMo-V2-Pro $1.50 · Relace Search $1.50 per 1M tokens (3:1 blend)
- Longest contextMiMo-V2-ProMiMo-V2-Pro 1,048,576 · Relace Search 256,000 · Qwen3 VL 235B A22B Thinking 131,072 tokens
- Widest inputsQwen3 VL 235B A22B ThinkingMiMo-V2-Pro: Text · Qwen3 VL 235B A22B Thinking: Text, Images · Relace Search: Text
- Self-hostingQwen3 VL 235B A22B ThinkingPublishes downloadable weights
| Measure | Weight | MiMo-V2-Pro | Qwen3 VL 235B A22B Thinking | Relace Search |
|---|---|---|---|---|
| Price | 50% | 40 | 42 | 40 |
| Inputs & features | 30% | 35 | 60 | 25 |
| Context window | 20% | 61 | 25 | 36 |
| Overall | 100% | 42/100 | 44/100 | 35/100 |
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 | MiMo-V2-ProXiaomi | Qwen3 VL 235B A22B ThinkingAlibaba (Qwen) | Relace SearchRelace |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.50 (best) | $1.00 |
| Output | $3.00 (best) | $3.95 | $3.00 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $1.50 | $1.36 (best) | $1.50 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 1 providers | Median of 5 providers | Median of 3 providers |
| Limits | |||
| Context window | 1,048,576 tokens (best) | 131,072 tokens | 256,000 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | — | — | — |
| API providers | 2 | 5 (best) | 3 |
| Released | Mar 18, 2026 | Sep 23, 2025 | Dec 8, 2025 |
| Knowledge cutoff | Dec 2024 | Mar 31, 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- MiMo-V2-Pro$16.00
- Qwen3 VL 235B A22B Thinking$12.90
- Relace Search$16.00
Which should you choose?
Which is better: MiMo-V2-Pro, Qwen3 VL 235B A22B Thinking or Relace Search?
It is close. Our weighted score puts them within 1 points (Qwen3 VL 235B A22B Thinking 44/100, MiMo-V2-Pro 42/100, Relace Search 35/100), so choose by what matters most for your work: Qwen3 VL 235B A22B Thinking on price and MiMo-V2-Pro 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, MiMo-V2-Pro, Qwen3 VL 235B A22B Thinking or Relace Search?
Qwen3 VL 235B A22B Thinking is cheaper at $0.50 input / $3.95 output per million tokens (median across 5 API providers). MiMo-V2-Pro costs $1.00 input / $3.00 output per million tokens (median across 1 API provider); Relace Search costs $1.00 input / $3.00 output per million tokens (median across 3 API providers). At a typical mix of three input tokens to one output token, that is $1.36 per million tokens for Qwen3 VL 235B A22B Thinking versus $1.50 for MiMo-V2-Pro (1.1× as much) and $1.50 for Relace Search (1.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiMo-V2-Pro has not been scored yet, Qwen3 VL 235B A22B Thinking has not been scored yet and Relace Search has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiMo-V2-Pro, Qwen3 VL 235B A22B Thinking and Relace Search yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.
Which has the bigger context window?
MiMo-V2-Pro has the largest context window at 1,048,576 tokens, against 256,000 for Relace Search and 131,072 for Qwen3 VL 235B A22B Thinking. Maximum output per response: MiMo-V2-Pro up to 131,072, Qwen3 VL 235B A22B Thinking up to 32,768, Relace Search up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiMo-V2-Pro accepts text; Qwen3 VL 235B A22B Thinking accepts text and images; Relace Search accepts text. Qwen3 VL 235B A22B Thinking handles the widest range of inputs.
Are any of these open source?
Qwen3 VL 235B A22B Thinking publishes its weights and can be self-hosted; MiMo-V2-Pro and Relace Search is proprietary.
Which is newer?
MiMo-V2-Pro is the newest, released Mar 18, 2026. Relace Search came out Dec 8, 2025; Qwen3 VL 235B A22B Thinking came out Sep 23, 2025. Knowledge cutoff: MiMo-V2-Pro Dec 2024, Qwen3 VL 235B A22B Thinking Mar 31, 2025.
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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