MiniMax-M2.5 vs DeepSeek OCR 2 vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 63 to 48 and 32 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.5
48/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
DeepSeekDeepSeek OCR 2
32/100- ECI—
- Price$0.89 / $1.47
- Context8K
- Our pick
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
GPT-5.4 nano is our pick
GPT-5.4 nano is the better all-round choice, scoring 63/100 against MiniMax-M2.5 (48) and DeepSeek OCR 2 (32). It leads on price, inputs & features and context window. 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 priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.5 $0.525 · DeepSeek OCR 2 $1.03 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nanoGPT-5.4 nano 400,000 · MiniMax-M2.5 204,800 · DeepSeek OCR 2 8,192 tokens
- Widest inputsDeepSeek OCR 2 and GPT-5.4 nanoMiniMax-M2.5: Text · DeepSeek OCR 2: Text, Images · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.5 and DeepSeek OCR 2Publishes downloadable weights
| Measure | Weight | MiniMax-M2.5 | DeepSeek OCR 2 | GPT-5.4 nano |
|---|---|---|---|---|
| Price | 50% | 63 | 49 | 66 |
| Inputs & features | 30% | 35 | 25 | 70 |
| Context window | 20% | 32 | 0 | 44 |
| Overall | 100% | 48/100 | 32/100 | 63/100 |
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-M2.5MiniMax | DeepSeek OCR 2DeepSeek | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.7 (best) | — | 145.8 |
| ECI rank | #66 of 148 (best) | — | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 78.5% |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 87.8% |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.89 | $0.20 (best) |
| Output | $1.20 (best) | $1.47 | $1.25 |
| Cached input | $0.03 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $1.03 | $0.463 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 1 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 8,192 tokens | 400,000 tokens (best) |
| Max output | 131,072 tokens (best) | 8,192 tokens | 128,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yes | No | Yeslow · medium · high · xhigh |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.5 | — | gpt-5.4-nano |
| API providers | 21 | 2 | 26 (best) |
| Released | Feb 12, 2026 | Jan 27, 2026 | Mar 17, 2026 |
| Knowledge cutoff | — | — | Aug 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.
- MiniMax-M2.5$5.40
- DeepSeek OCR 2$11.83
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.5, DeepSeek OCR 2 or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 63/100 against MiniMax-M2.5 (48) and DeepSeek OCR 2 (32). It leads on price, inputs & features and context window. 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, DeepSeek OCR 2 or GPT-5.4 nano?
GPT-5.4 nano is cheaper at $0.20 input / $1.25 output per million tokens (official OpenAI API price). MiniMax-M2.5 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price); DeepSeek OCR 2 costs $0.89 input / $1.47 output per million tokens (median across 1 API provider). At a typical mix of three input tokens to one output token, that is $0.463 per million tokens for GPT-5.4 nano versus $0.525 for MiniMax-M2.5 (1.1× as much) and $1.03 for DeepSeek OCR 2 (2.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.5 has an ECI of 146.7, DeepSeek OCR 2 has not been scored yet and GPT-5.4 nano has an ECI of 145.8.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.5, DeepSeek OCR 2 and GPT-5.4 nano 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?
GPT-5.4 nano has the largest context window at 400,000 tokens, against 204,800 for MiniMax-M2.5 and 8,192 for DeepSeek OCR 2. Maximum output per response: MiniMax-M2.5 up to 131,072, DeepSeek OCR 2 up to 8,192, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.5 accepts text; DeepSeek OCR 2 accepts text and images; GPT-5.4 nano accepts text and images. DeepSeek OCR 2 handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 and DeepSeek OCR 2 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. MiniMax-M2.5 came out Feb 12, 2026; DeepSeek OCR 2 came out Jan 27, 2026. Knowledge cutoff: GPT-5.4 nano Aug 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 Oct 4, 2026.
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