GLM-5 vs MiMo-V2.5-Pro vs Qwen3.5 397B-A17B
Too close to call on our weighted score (Qwen3.5 397B-A17B 56, MiMo-V2.5-Pro 54, GLM-5 37). The right pick depends on what you value most.
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
XiaomiMiMo-V2.5-Pro
54/100- ECI—
- Price$0.435 / $0.87
- Context1.05M
Alibaba (Qwen)Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
Too close to call
It is close. Our weighted score puts them within 2 points (Qwen3.5 397B-A17B 56/100, MiMo-V2.5-Pro 54/100, GLM-5 37/100), so choose by what matters most for your work: MiMo-V2.5-Pro on price and MiMo-V2.5-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 priceMiMo-V2.5-ProMiMo-V2.5-Pro $0.544 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextMiMo-V2.5-ProMiMo-V2.5-Pro 1,048,576 · Qwen3.5 397B-A17B 262,144 · GLM-5 204,800 tokens
- Widest inputsQwen3.5 397B-A17BGLM-5: Text · MiMo-V2.5-Pro: Text · Qwen3.5 397B-A17B: Text, Images, Audio, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | GLM-5 | MiMo-V2.5-Pro | Qwen3.5 397B-A17B |
|---|---|---|---|---|
| Price | 50% | 41 | 62 | 44 |
| Inputs & features | 30% | 35 | 35 | 90 |
| Context window | 20% | 32 | 61 | 37 |
| Overall | 100% | 37/100 | 54/100 | 56/100 |
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 | GLM-5Z.ai (Zhipu) | MiMo-V2.5-ProXiaomi | Qwen3.5 397B-A17BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | 146.7 (best) |
| ECI rank | #74 of 148 | — | #67 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 87.8% (best) | — | 86.4% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 31.2% |
| OTIS Mock AIME 2024–2025Competition mathematics | 80.0% | — | 88.9% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 72.1% | — | — |
| Price per million tokens | |||
| Input | $1.00 | $0.435 (best) | $0.60 |
| Output | $3.20 | $0.87 (best) | $3.60 |
| Cached input | $0.20 | $0.0036 (best) | — |
| Blended (3:1) | $1.55 | $0.544 (best) | $1.35 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Z.AI API | Official Xiaomi API | Official Alibaba API |
| Limits | |||
| Context window | 204,800 tokens | 1,048,576 tokens (best) | 262,144 tokens |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | glm-5 | mimo-v2.5-pro | qwen3.5-397b-a17b |
| API providers | 27 (best) | 20 | 23 |
| Released | Feb 12, 2026 | Apr 22, 2026 | Feb 15, 2026 |
| Knowledge cutoff | — | Dec 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GLM-5$16.40
- MiMo-V2.5-Pro$6.09
- Qwen3.5 397B-A17B$13.20
Which should you choose?
Which is better: GLM-5, MiMo-V2.5-Pro or Qwen3.5 397B-A17B?
It is close. Our weighted score puts them within 2 points (Qwen3.5 397B-A17B 56/100, MiMo-V2.5-Pro 54/100, GLM-5 37/100), so choose by what matters most for your work: MiMo-V2.5-Pro on price and MiMo-V2.5-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, GLM-5, MiMo-V2.5-Pro or Qwen3.5 397B-A17B?
MiMo-V2.5-Pro is cheaper at $0.435 input / $0.87 output per million tokens (official Xiaomi API price). Qwen3.5 397B-A17B costs $0.60 input / $3.60 output per million tokens (official Alibaba API price); GLM-5 costs $1.00 input / $3.20 output per million tokens (official Z.AI API price). At a typical mix of three input tokens to one output token, that is $0.544 per million tokens for MiMo-V2.5-Pro versus $1.35 for Qwen3.5 397B-A17B (2.5× as much) and $1.55 for GLM-5 (2.9× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GLM-5 has an ECI of 145.8, MiMo-V2.5-Pro has not been scored yet and Qwen3.5 397B-A17B has an ECI of 146.7.
Which is better for coding?
There are no published SWE-bench Verified results for MiMo-V2.5-Pro and Qwen3.5 397B-A17B 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-V2.5-Pro has the largest context window at 1,048,576 tokens, against 262,144 for Qwen3.5 397B-A17B and 204,800 for GLM-5. Maximum output per response: GLM-5 up to 131,072, MiMo-V2.5-Pro up to 131,072, Qwen3.5 397B-A17B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
GLM-5 accepts text; MiMo-V2.5-Pro accepts text; Qwen3.5 397B-A17B accepts text, images, audio and video. Qwen3.5 397B-A17B handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights, so you can self-host them.
Which is newer?
MiMo-V2.5-Pro is the newest, released Apr 22, 2026. Qwen3.5 397B-A17B came out Feb 15, 2026; GLM-5 came out Feb 12, 2026. Knowledge cutoff: MiMo-V2.5-Pro Dec 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.
