MiniMax-M2.5-highspeed vs GLM-5 vs DeepSeek OCR 2
MiniMax-M2.5-highspeed comes out ahead, 41 to 37 and 32 on our weighted score.
- Our pick
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
DeepSeekDeepSeek OCR 2
32/100- ECI—
- Price$0.89 / $1.47
- Context8K
MiniMax-M2.5-highspeed is our pick
MiniMax-M2.5-highspeed is the better all-round choice, scoring 41/100 against GLM-5 (37) and DeepSeek OCR 2 (32). 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 priceDeepSeek OCR 2DeepSeek OCR 2 $1.03 · MiniMax-M2.5-highspeed $1.05 · GLM-5 $1.55 per 1M tokens (3:1 blend)
- Longest contextMiniMax-M2.5-highspeed and GLM-5MiniMax-M2.5-highspeed 204,800 · GLM-5 204,800 · DeepSeek OCR 2 8,192 tokens
- Widest inputsDeepSeek OCR 2MiniMax-M2.5-highspeed: Text · GLM-5: Text · DeepSeek OCR 2: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | MiniMax-M2.5-highspeed | GLM-5 | DeepSeek OCR 2 |
|---|---|---|---|---|
| Price | 50% | 49 | 41 | 49 |
| Inputs & features | 30% | 35 | 35 | 25 |
| Context window | 20% | 32 | 32 | 0 |
| Overall | 100% | 41/100 | 37/100 | 32/100 |
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 | GLM-5Z.ai (Zhipu) | DeepSeek OCR 2DeepSeek |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 145.8 | — |
| ECI rank | — | #74 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 87.8% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 80.0% | — |
| SWE-bench VerifiedFixing real GitHub issues | — | 72.1% | — |
| Price per million tokens | |||
| Input | $0.60 (best) | $1.00 | $0.89 |
| Output | $2.40 | $3.20 | $1.47 (best) |
| Cached input | $0.06 (best) | $0.20 | — |
| Blended (3:1) | $1.05 | $1.55 | $1.03 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Official Z.AI API | Median of 1 providers |
| Limits | |||
| Context window | 204,800 tokens (best) | 204,800 tokens (best) | 8,192 tokens |
| Max output | 131,072 tokens (best) | 131,072 tokens (best) | 8,192 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | Yes | No |
| Tool calling | Yes | Yes | No |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | MiniMax-M2.5-highspeed | glm-5 | — |
| API providers | 7 | 27 (best) | 2 |
| Released | Feb 13, 2026 | Feb 12, 2026 | Jan 27, 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
- GLM-5$16.40
- DeepSeek OCR 2$11.83
Which should you choose?
Which is better: MiniMax-M2.5-highspeed, GLM-5 or DeepSeek OCR 2?
MiniMax-M2.5-highspeed is the better all-round choice, scoring 41/100 against GLM-5 (37) and DeepSeek OCR 2 (32). 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, GLM-5 or DeepSeek OCR 2?
DeepSeek OCR 2 is cheaper at $0.89 input / $1.47 output per million tokens (median across 1 API provider). MiniMax-M2.5-highspeed costs $0.60 input / $2.40 output per million tokens (official MiniMax (minimax.io) 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 $1.03 per million tokens for DeepSeek OCR 2 versus $1.05 for MiniMax-M2.5-highspeed (1× 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. MiniMax-M2.5-highspeed has not been scored yet, GLM-5 has an ECI of 145.8 and DeepSeek OCR 2 has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.5-highspeed and DeepSeek OCR 2 yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that DeepSeek OCR 2 does not support tool calling, which most coding agents need.
Which has the bigger context window?
MiniMax-M2.5-highspeed and GLM-5 have the largest context windows (204,800 and 204,800 tokens), against 8,192 for DeepSeek OCR 2. Maximum output per response: MiniMax-M2.5-highspeed up to 131,072, GLM-5 up to 131,072, DeepSeek OCR 2 up to 8,192 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.5-highspeed accepts text; GLM-5 accepts text; DeepSeek OCR 2 accepts text and images. DeepSeek OCR 2 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?
MiniMax-M2.5-highspeed is the newest, released Feb 13, 2026. GLM-5 came out Feb 12, 2026; DeepSeek OCR 2 came out Jan 27, 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.
