MiniMax-M2.5-highspeed vs Qwen3.5 122B-A10B vs Qwen3.5 9B
Qwen3.5 9B comes out ahead, 79 to 58 and 41 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.5-highspeed
41/100- ECI—
- Price$0.60 / $2.40
- Context205K
Alibaba (Qwen)Qwen3.5 122B-A10B
58/100- ECI—
- Price$0.40 / $3.20
- Context262K
- Our pickAlibaba (Qwen)
Qwen3.5 9B
79/100- ECI139.5
- Price$0.10 / $0.15
- Context262K
Qwen3.5 9B is our pick
Qwen3.5 9B is the better all-round choice, scoring 79/100 against Qwen3.5 122B-A10B (58) and MiniMax-M2.5-highspeed (41). It leads on price. Qwen3.5 122B-A10B wins on inputs & features. 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 priceQwen3.5 9BQwen3.5 9B $0.113 · MiniMax-M2.5-highspeed $1.05 · Qwen3.5 122B-A10B $1.10 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 122B-A10B and Qwen3.5 9BQwen3.5 122B-A10B 262,144 · Qwen3.5 9B 262,144 · MiniMax-M2.5-highspeed 204,800 tokens
- Widest inputsQwen3.5 122B-A10BMiniMax-M2.5-highspeed: Text · Qwen3.5 122B-A10B: Text, Images, Audio, Video · Qwen3.5 9B: Text, Images, Video
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | MiniMax-M2.5-highspeed | Qwen3.5 122B-A10B | Qwen3.5 9B |
|---|---|---|---|---|
| Price | 50% | 49 | 48 | 95 |
| Inputs & features | 30% | 35 | 90 | 80 |
| Context window | 20% | 32 | 37 | 37 |
| Overall | 100% | 41/100 | 58/100 | 79/100 |
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 | MiniMax-M2.5-highspeedMiniMax | Qwen3.5 122B-A10BAlibaba (Qwen) | Qwen3.5 9BAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 139.5 |
| ECI rank | — | — | #101 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 79.0% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 61.7% |
| Price per million tokens | |||
| Input | $0.60 | $0.40 | $0.10 (best) |
| Output | $2.40 | $3.20 | $0.15 (best) |
| Cached input | $0.06 | — | — |
| Blended (3:1) | $1.05 | $1.10 | $0.113 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Alibaba API | Median of 14 providers |
| Limits | |||
| Context window | 204,800 tokens | 262,144 tokens (best) | 262,144 tokens (best) |
| Max output | 131,072 tokens (best) | 65,536 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | Yes | No |
| Video | No | Yes | Yes |
| Reasoning | Yes | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | Yes |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | MiniMax-M2.5-highspeed | qwen3.5-122b-a10b | — |
| API providers | 7 | 19 (best) | 15 |
| Released | Feb 13, 2026 | Feb 23, 2026 | Feb 23, 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.
- MiniMax-M2.5-highspeed$10.80
- Qwen3.5 122B-A10B$10.40
- Qwen3.5 9B$1.30
Which should you choose?
Which is better: MiniMax-M2.5-highspeed, Qwen3.5 122B-A10B or Qwen3.5 9B?
Qwen3.5 9B is the better all-round choice, scoring 79/100 against Qwen3.5 122B-A10B (58) and MiniMax-M2.5-highspeed (41). It leads on price. Qwen3.5 122B-A10B wins on inputs & features. 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, MiniMax-M2.5-highspeed, Qwen3.5 122B-A10B or Qwen3.5 9B?
Qwen3.5 9B is cheaper at $0.10 input / $0.15 output per million tokens (median across 14 API providers). MiniMax-M2.5-highspeed costs $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) API price); Qwen3.5 122B-A10B costs $0.40 input / $3.20 output per million tokens (official Alibaba API price). At a typical mix of three input tokens to one output token, that is $0.113 per million tokens for Qwen3.5 9B versus $1.05 for MiniMax-M2.5-highspeed (9.3× as much) and $1.10 for Qwen3.5 122B-A10B (9.8× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.5-highspeed has not been scored yet, Qwen3.5 122B-A10B has not been scored yet and Qwen3.5 9B has an ECI of 139.5.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.5-highspeed, Qwen3.5 122B-A10B and Qwen3.5 9B 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 122B-A10B and Qwen3.5 9B have the largest context windows (262,144 and 262,144 tokens), against 204,800 for MiniMax-M2.5-highspeed. Maximum output per response: MiniMax-M2.5-highspeed up to 131,072, Qwen3.5 122B-A10B up to 65,536, Qwen3.5 9B up to 65,536 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.5-highspeed accepts text; Qwen3.5 122B-A10B accepts text, images, audio and video; Qwen3.5 9B accepts text, images and video. Qwen3.5 122B-A10B 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 122B-A10B is the newest, released Feb 23, 2026. Qwen3.5 9B came out Feb 23, 2026; MiniMax-M2.5-highspeed came out Feb 13, 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.
