MiniMax-M2.5 vs Llama 4 Scout 17B Instruct vs GPT-5.4 nano
GPT-5.4 nano comes out ahead, 68 to 62 and 61 on our weighted score, though Llama 4 Scout 17B Instruct is 26% cheaper per token.
MiniMaxMiniMax-M2.5
61/100- ECI146.7
- Price$0.30 / $1.20
- Context205K
MetaLlama 4 Scout 17B Instruct
62/100- ECI129.7
- Price$0.225 / $0.69
- Context10M
- Our pick
OpenAIGPT-5.4 nano
68/100- 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 68/100 against Llama 4 Scout 17B Instruct (62) and MiniMax-M2.5 (61). It leads on inputs & features. Llama 4 Scout 17B Instruct wins on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- CapabilityMiniMax-M2.5Capabilities Index (ECI): MiniMax-M2.5 146.7 · GPT-5.4 nano 145.8 · Llama 4 Scout 17B Instruct 129.7
- Lowest priceLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct $0.341 · GPT-5.4 nano $0.463 · MiniMax-M2.5 $0.525 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · GPT-5.4 nano 400,000 · MiniMax-M2.5 204,800 tokens
- Widest inputsLlama 4 Scout 17B Instruct and GPT-5.4 nanoMiniMax-M2.5: Text · Llama 4 Scout 17B Instruct: Text, Images · GPT-5.4 nano: Text, Images
- Self-hostingMiniMax-M2.5 and Llama 4 Scout 17B InstructPublishes downloadable weights
| Measure | Weight | MiniMax-M2.5 | Llama 4 Scout 17B Instruct | GPT-5.4 nano |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 52 | 73 |
| Price | 25% | 63 | 72 | 66 |
| Inputs & features | 15% | 35 | 50 | 70 |
| Context window | 10% | 32 | 100 | 44 |
| Overall | 100% | 61/100 | 62/100 | 68/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | MiniMax-M2.5MiniMax | Llama 4 Scout 17B InstructMeta | GPT-5.4 nanoOpenAI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.7 (best) | 129.7 | 145.8 |
| ECI rank | #66 of 148 (best) | #126 of 148 | #75 of 148 |
| GPQA DiamondGraduate-level science questions | — | 51.8% | 78.5% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | — | 44.9% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 7.8% | 87.8% (best) |
| SimpleQA VerifiedShort factual questions | — | — | 11.7% |
| Price per million tokens | |||
| Input | $0.30 | $0.225 | $0.20 (best) |
| Output | $1.20 | $0.69 (best) | $1.25 |
| Cached input | $0.03 | — | $0.02 (best) |
| Blended (3:1) | $0.525 | $0.341 (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 | 10,000,000 tokens (best) | 400,000 tokens |
| Max output | 131,072 tokens (best) | 16,384 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 | Yes | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | MiniMax-M2.5 | — | gpt-5.4-nano |
| API providers | 21 | 4 | 26 (best) |
| Released | Feb 12, 2026 | Apr 5, 2025 | Mar 17, 2026 |
| Knowledge cutoff | — | Aug 2024 | 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
- Llama 4 Scout 17B Instruct$3.63
- GPT-5.4 nano$4.50
Which should you choose?
Which is better: MiniMax-M2.5, Llama 4 Scout 17B Instruct or GPT-5.4 nano?
GPT-5.4 nano is the better all-round choice, scoring 68/100 against Llama 4 Scout 17B Instruct (62) and MiniMax-M2.5 (61). It leads on inputs & features. Llama 4 Scout 17B Instruct wins on price and context window. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, MiniMax-M2.5, Llama 4 Scout 17B Instruct or GPT-5.4 nano?
Llama 4 Scout 17B Instruct is cheaper at $0.225 input / $0.69 output per million tokens (median across 4 API providers). GPT-5.4 nano costs $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). At a typical mix of three input tokens to one output token, that is $0.341 per million tokens for Llama 4 Scout 17B Instruct versus $0.463 for GPT-5.4 nano (1.4× as much) and $0.525 for MiniMax-M2.5 (1.5× as much).
Which scores higher on benchmarks?
MiniMax-M2.5 scores higher on the Capabilities Index (ECI): MiniMax-M2.5 146.7 (#66 of 148), GPT-5.4 nano 145.8 (#75 of 148) and Llama 4 Scout 17B Instruct 129.7 (#126 of 148). The confidence ranges of the top two overlap (142.3–147.9 vs 143.2–147.7), so treat the gap as small.
Which is better for coding?
There are no published SWE-bench Verified results for MiniMax-M2.5, Llama 4 Scout 17B Instruct and GPT-5.4 nano yet, so there is no like-for-like coding score. On overall capability, MiniMax-M2.5 leads, which tends to carry over to coding, but test on your own codebase. All three support tool calling for agent workflows.
Which has the bigger context window?
Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 tokens, against 400,000 for GPT-5.4 nano and 204,800 for MiniMax-M2.5. Maximum output per response: MiniMax-M2.5 up to 131,072, Llama 4 Scout 17B Instruct up to 16,384, GPT-5.4 nano up to 128,000 tokens.
Which can read images, PDFs, audio or video?
MiniMax-M2.5 accepts text; Llama 4 Scout 17B Instruct accepts text and images; GPT-5.4 nano accepts text and images. Llama 4 Scout 17B Instruct handles the widest range of inputs.
Are any of these open source?
MiniMax-M2.5 and Llama 4 Scout 17B Instruct 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; Llama 4 Scout 17B Instruct came out Apr 5, 2025. Knowledge cutoff: Llama 4 Scout 17B Instruct Aug 2024, 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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