GPT-5.4 nano vs GPT-5-Codex vs MiniMax-M2.5
GPT-5.4 nano comes out ahead, 63 to 48 and 42 on our weighted score, and it is the cheaper option too.
- Our pick
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
- OpenAI
GPT-5-Codex
42/100- ECI—
- Price$1.25 / $10.00
- Context400K
MiniMaxMiniMax-M2.5
48/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
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 GPT-5-Codex (42). It leads 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 priceGPT-5.4 nanoGPT-5.4 nano $0.463 · MiniMax-M2.5 $0.525 · GPT-5-Codex $3.44 per 1M tokens (3:1 blend)
- Longest contextGPT-5.4 nano and GPT-5-CodexGPT-5.4 nano 400,000 · GPT-5-Codex 400,000 · MiniMax-M2.5 204,800 tokens
- Widest inputsGPT-5.4 nano and GPT-5-CodexGPT-5.4 nano: Text, Images · GPT-5-Codex: Text, Images · MiniMax-M2.5: Text
- Self-hostingMiniMax-M2.5Publishes downloadable weights
| Measure | Weight | GPT-5.4 nano | GPT-5-Codex | MiniMax-M2.5 |
|---|---|---|---|---|
| Price | 50% | 66 | 24 | 63 |
| Inputs & features | 30% | 70 | 70 | 35 |
| Context window | 20% | 44 | 44 | 32 |
| Overall | 100% | 63/100 | 42/100 | 48/100 |
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 | GPT-5.4 nanoOpenAI | GPT-5-CodexOpenAI | MiniMax-M2.5MiniMax |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.8 | — | 146.7 (best) |
| ECI rank | #75 of 148 | — | #66 of 148 (best) |
| 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 | $0.20 (best) | $1.25 | $0.30 |
| Output | $1.25 | $10.00 | $1.20 (best) |
| Cached input | $0.02 (best) | — | $0.03 |
| Blended (3:1) | $0.463 (best) | $3.44 | $0.525 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official OpenAI API | Median of 3 providers | Official MiniMax (minimax.io) API |
| Limits | |||
| Context window | 400,000 tokens (best) | 400,000 tokens (best) | 204,800 tokens |
| Max output | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high · xhigh | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | Yes | No |
| Availability | |||
| Weights | Proprietary | Proprietary | Open |
| API model ID | gpt-5.4-nano | — | MiniMax-M2.5 |
| API providers | 26 (best) | 3 | 21 |
| Released | Mar 17, 2026 | Sep 15, 2025 | Feb 12, 2026 |
| Knowledge cutoff | Aug 31, 2025 | Sep 30, 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.4 nano$4.50
- GPT-5-Codex$32.50
- MiniMax-M2.5$5.40
Which should you choose?
Which is better: GPT-5.4 nano, GPT-5-Codex or MiniMax-M2.5?
GPT-5.4 nano is the better all-round choice, scoring 63/100 against MiniMax-M2.5 (48) and GPT-5-Codex (42). It leads 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, GPT-5.4 nano, GPT-5-Codex or MiniMax-M2.5?
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); GPT-5-Codex costs $1.25 input / $10.00 output per million tokens (median across 3 API providers). 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 $3.44 for GPT-5-Codex (7.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.4 nano has an ECI of 145.8, GPT-5-Codex has not been scored yet and MiniMax-M2.5 has an ECI of 146.7.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.4 nano, GPT-5-Codex and MiniMax-M2.5 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?
GPT-5.4 nano and GPT-5-Codex have the largest context windows (400,000 and 400,000 tokens), against 204,800 for MiniMax-M2.5. Maximum output per response: GPT-5.4 nano up to 128,000, GPT-5-Codex up to 128,000, MiniMax-M2.5 up to 131,072 tokens.
Which can read images, PDFs, audio or video?
GPT-5.4 nano accepts text and images; GPT-5-Codex accepts text and images; MiniMax-M2.5 accepts text. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 publishes its weights and can be self-hosted; GPT-5.4 nano and GPT-5-Codex is proprietary.
Which is newer?
GPT-5.4 nano is the newest, released Mar 17, 2026. MiniMax-M2.5 came out Feb 12, 2026; GPT-5-Codex came out Sep 15, 2025. Knowledge cutoff: GPT-5.4 nano Aug 31, 2025, GPT-5-Codex Sep 30, 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.
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