MiniMax-M2.7 vs Laguna S 2.1 vs GPT-5.4 nano
Laguna S 2.1 comes out ahead, 69 to 63 and 48 on our weighted score, and it is the cheaper option too.
MiniMaxMiniMax-M2.7
48/100- ECI145.9
- Price$0.30 / $1.20
- Context205K
- Our pick
PoolsideLaguna S 2.1
69/100- ECI—
- Price$0.10 / $0.20
- Context1.05M
OpenAIGPT-5.4 nano
63/100- ECI145.8
- Price$0.20 / $1.25
- Context400K
Laguna S 2.1 is our pick
Laguna S 2.1 is the better all-round choice, scoring 69/100 against GPT-5.4 nano (63) and MiniMax-M2.7 (48). It leads on price and context window. GPT-5.4 nano 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 priceLaguna S 2.1Laguna S 2.1 $0.125 · GPT-5.4 nano $0.463 · MiniMax-M2.7 $0.525 per 1M tokens (3:1 blend)
- Longest contextLaguna S 2.1Laguna S 2.1 1,048,576 · GPT-5.4 nano 400,000 · MiniMax-M2.7 204,800 tokens
- Widest inputsGPT-5.4 nanoMiniMax-M2.7: Text · Laguna S 2.1: Text · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.7 and Laguna S 2.1Publishes downloadable weights
| Measure | Weight | MiniMax-M2.7 | Laguna S 2.1 | GPT-5.4 nano |
|---|---|---|---|---|
| Price | 50% | 63 | 93 | 66 |
| Inputs & features | 30% | 35 | 35 | 70 |
| Context window | 20% | 32 | 61 | 44 |
| Overall | 100% | 48/100 | 69/100 | 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-M2.7MiniMax | Laguna S 2.1Poolside | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.9 (best) | — | 145.8 |
| ECI rank | #73 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 | $0.10 (best) | $0.20 |
| Output | $1.20 | $0.20 (best) | $1.25 |
| Cached input | $0.06 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.125 (best) | $0.463 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official MiniMax (minimax.io) API | Median of 4 providers | Official OpenAI API |
| Limits | |||
| Context window | 204,800 tokens | 1,048,576 tokens (best) | 400,000 tokens |
| Max output | 131,072 tokens (best) | 32,768 tokens | 128,000 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 | Yeslow · medium · high · xhigh |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.7 | poolside/laguna-s-2.1 | gpt-5.4-nano |
| API providers | 29 (best) | 6 | 26 |
| Released | Mar 18, 2026 | Jul 21, 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-M2.7$5.40
- Laguna S 2.1$1.40
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.7, Laguna S 2.1 or GPT-5.4 nano?
Laguna S 2.1 is the better all-round choice, scoring 69/100 against GPT-5.4 nano (63) and MiniMax-M2.7 (48). It leads on price and context window. GPT-5.4 nano 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-M2.7, Laguna S 2.1 or GPT-5.4 nano?
Laguna S 2.1 is cheaper at $0.10 input / $0.20 output per million tokens (median across 4 API providers; free on Poolside). GPT-5.4 nano costs $0.20 input / $1.25 output per million tokens (official OpenAI API price); MiniMax-M2.7 costs $0.30 input / $1.20 output per million tokens (official MiniMax (minimax.io) API price). At a typical mix of three input tokens to one output token, that is $0.125 per million tokens for Laguna S 2.1 versus $0.463 for GPT-5.4 nano (3.7× as much) and $0.525 for MiniMax-M2.7 (4.2× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. MiniMax-M2.7 has an ECI of 145.9, Laguna S 2.1 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-M2.7, Laguna S 2.1 and GPT-5.4 nano 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?
Laguna S 2.1 has the largest context window at 1,048,576 tokens, against 400,000 for GPT-5.4 nano and 204,800 for MiniMax-M2.7. Maximum output per response: MiniMax-M2.7 up to 131,072, Laguna S 2.1 up to 32,768, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.7 accepts text; Laguna S 2.1 accepts text; GPT-5.4 nano accepts text and images. GPT-5.4 nano handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.7 and Laguna S 2.1 publishes its weights and can be self-hosted; GPT-5.4 nano is proprietary.
Which is newer?
Laguna S 2.1 is the newest, released Jul 21, 2026. MiniMax-M2.7 came out Mar 18, 2026; GPT-5.4 nano came out Mar 17, 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.
