Qwen3.5 397B-A17B vs MiniMax-M2.5-highspeed vs GLM-5
Qwen3.5 397B-A17B comes out ahead, 56 to 41 and 37 on our weighted score, though MiniMax-M2.5-highspeed is 22% cheaper per token.
- Our pick
Alibaba (Qwen)Qwen3.5 397B-A17B
56/100- ECI146.7
- Price$0.60 / $3.60
- Context262K
MiniMaxMiniMax-M2.5-highspeed
41/100- ECI—
- Price$0.60 / $2.40
- Context205K
Z.ai (Zhipu)GLM-5
37/100- ECI145.8
- Price$1.00 / $3.20
- Context205K
Qwen3.5 397B-A17B is our pick
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against MiniMax-M2.5-highspeed (41) and GLM-5 (37). It leads on inputs & features and context window. MiniMax-M2.5-highspeed wins 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 priceMiniMax-M2.5-highspeedMiniMax-M2.5-highspeed $1.05 · Qwen3.5 397B-A17B $1.35 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 397B-A17BQwen3.5 397B-A17B 262,144 · MiniMax-M2.5-highspeed 204,800 · GLM-5 204,800 tokens
- Widest inputsQwen3.5 397B-A17BQwen3.5 397B-A17B: Text, Images, Audio, Video · MiniMax-M2.5-highspeed: Text · GLM-5: Text
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Qwen3.5 397B-A17B | MiniMax-M2.5-highspeed | GLM-5 |
|---|---|---|---|---|
| Price | 50% | 44 | 49 | 41 |
| Inputs & features | 30% | 90 | 35 | 35 |
| Context window | 20% | 37 | 32 | 32 |
| Overall | 100% | 56/100 | 41/100 | 37/100 |
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 397B-A17BAlibaba (Qwen) | MiniMax-M2.5-highspeedMiniMax | GLM-5Z.ai (Zhipu) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.7 (best) | — | 145.8 |
| ECI rank | #67 of 148 (best) | — | #74 of 148 |
| GPQA DiamondGraduate-level science questions | 86.4% | — | 87.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 31.2% | — | — |
| OTIS Mock AIME 2024–2025Competition mathematics | 88.9% (best) | — | 80.0% |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 72.1% |
| Price per million tokens | |||
| Input | $0.60 (best) | $0.60 (best) | $1.00 |
| Output | $3.60 | $2.40 (best) | $3.20 |
| Cached input | — | $0.06 (best) | $0.20 |
| Blended (3:1) | $1.35 | $1.05 (best) | $1.55 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Alibaba API | Official MiniMax (minimax.io) API | Official Z.AI API |
| Limits | |||
| Context window | 262,144 tokens (best) | 204,800 tokens | 204,800 tokens |
| Max output | 65,536 tokens | 131,072 tokens (best) | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | No |
| PDFs | No | No | No |
| Audio | Yes | No | No |
| Video | Yes | No | No |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | qwen3.5-397b-a17b | MiniMax-M2.5-highspeed | glm-5 |
| API providers | 23 | 7 | 27 (best) |
| Released | Feb 15, 2026 | Feb 13, 2026 | Feb 12, 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 397B-A17B$13.20
- MiniMax-M2.5-highspeed$10.80
- GLM-5$16.40
Which should you choose?
Which is better: Qwen3.5 397B-A17B, MiniMax-M2.5-highspeed or GLM-5?
Qwen3.5 397B-A17B is the better all-round choice, scoring 56/100 against MiniMax-M2.5-highspeed (41) and GLM-5 (37). It leads on inputs & features and context window. MiniMax-M2.5-highspeed wins 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 397B-A17B, MiniMax-M2.5-highspeed or GLM-5?
MiniMax-M2.5-highspeed is cheaper at $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) 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 $1.05 per million tokens for MiniMax-M2.5-highspeed versus $1.35 for Qwen3.5 397B-A17B (1.3× as much) and $1.55 for GLM-5 (1.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Qwen3.5 397B-A17B has an ECI of 146.7, MiniMax-M2.5-highspeed has not been scored yet and GLM-5 has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for Qwen3.5 397B-A17B and MiniMax-M2.5-highspeed 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?
Qwen3.5 397B-A17B has the largest context window at 262,144 tokens, against 204,800 for MiniMax-M2.5-highspeed and 204,800 for GLM-5. Maximum output per response: Qwen3.5 397B-A17B up to 65,536, MiniMax-M2.5-highspeed up to 131,072, GLM-5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
Qwen3.5 397B-A17B accepts text, images, audio and video; MiniMax-M2.5-highspeed accepts text; GLM-5 accepts text. 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?
Qwen3.5 397B-A17B is the newest, released Feb 15, 2026. MiniMax-M2.5-highspeed came out Feb 13, 2026; GLM-5 came out Feb 12, 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 Oct 4, 2026.
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