Llama 4 Scout vs MiniMax M2.5
Llama 4 Scout (Meta, April 2025) and MiniMax M2.5 (MiniMax, February 2026) sit at different points on the price-vs-quality curve. Scout is a mixture-of-experts model built for massive context and multimodal input at a low price. M2.5 is a reasoning-mandatory model tuned for real-world coding and agentic tasks, priced higher but scoring far ahead on every head-to-head benchmark category both models share. Both run on OpenKey with one key and a flat 3% fee on top of provider list price.
Spec vs spec
| Spec | Llama 4 Scout | MiniMax M2.5 |
|---|---|---|
| Context window | 10M | 205K |
| Max output | 16K | 197K |
| Input modalities | text, image | text |
| Output modalities | text | text |
| Knowledge cutoff | Aug 31, 2024 | — |
| Released | Apr 5, 2025 | Feb 12, 2026 |
| Reasoning | — | always on |
Pricing
Per 1M tokens. Provider price plus the flat 3% fee — the sum is what you pay.
meta-llama/llama-4-scout
Input · 1M tokens
$0.100 + 3%$0.103
Output · 1M tokens
$0.300 + 3%$0.309
FEE — FLAT, EVERY MODEL3%
minimax/minimax-m2.5
Input · 1M tokens
$0.120 + 3%$0.124
Output · 1M tokens
$0.480 + 3%$0.494
FEE — FLAT, EVERY MODEL3%
One workload, priced on both
10M input + 2M output tokens at each model's price, flat 3% fee included.
meta-llama/llama-4-scoutCheaper
$1.65
$1.60 provider + 3%
minimax/minimax-m2.5
$2.22
$2.16 provider + 3%
Benchmarks
Design Arena categories where both models have results. Higher Elo and lower rank win.
| Llama 4 Scout | MiniMax M2.5 | |||
|---|---|---|---|---|
| Category | Elo | Rank | Elo | Rank |
| Code | 839 | #106 | 1256 | #29 |
| Data viz | 940 | #96 | 1216 | #40 |
| Game dev | 838 | #105 | 1242 | #33 |
| UI components | 824 | #100 | 1228 | #37 |
| Websites | 793 | #112 | 1265 | #26 |
Head-to-head preference voting. How we filter and rank
Pricing math
Provider list price: Scout runs $0.10/M input, $0.30/M output. M2.5 runs $0.12/M input, $0.48/M output. On OpenKey that's provider price × 1.03: Scout becomes $0.103/M input, $0.309/M output; M2.5 becomes $0.1236/M input, $0.4944/M output.
For a 10M-input / 2M-output workload, Scout costs $1.60 and M2.5 costs $2.16 — a $0.56 gap, with M2.5 at roughly 35% more expensive for that mix. Scout's input price is about 83% of M2.5's (input_price_ratio: 0.83), so the cost gap widens more on output-heavy jobs since M2.5's completion rate is 60% higher than Scout's.
Coding and design benchmarks
Design Arena elo tells a one-sided story. In codecategories, M2.5 scores 1256 (rank 29, 56.8% win rate) against Scout's 839 (rank 106, 26.6% win rate) — a 417-point gap. In dataviz M2.5 hits 1216 vs Scout's 940. In gamedev, 1242 vs 838. In uicomponent, 1228 vs 824. In website, 1265 vs 793. M2.5 also has design_arena entries in 3d and svg categories where Scout has none listed.
On artificial_analysis metrics, Scout posts an intelligence_index of 10, coding_index of 8.2, and agentic_index of 1.1 — no equivalent artificial_analysis figures are recorded for M2.5, so that comparison isn't apples-to-apples, but the Design Arena data alone is decisive for coding-adjacent work.
Context and modality
Scout's context window is 10,000,000 tokens against M2.5's 204,800 — a context_ratio of 48.83x in Scout's favor. If your workload needs to ingest an entire codebase, a long transcript archive, or multi-document retrieval in one pass, Scout is the only one of the two that fits.
Modality also differs: Scout accepts text and image input (text+image->text), while M2.5 is text-only (text->text). Max completion tokens flip the other way — M2.5 allows up to 196,608 output tokens per call versus Scout's 16,384, useful if you need long generated output (long code files, full reports) rather than long input.
Reasoning and tool use
M2.5 has mandatory reasoning (reasoning.mandatory: true) and supports reasoning_effort and include_reasoning parameters, plus parallel_tool_calls, logprobs, and top_logprobs — a parameter set built for agentic pipelines that need visibility into the reasoning trace and tool orchestration. Scout's supported parameters cover the standard set (temperature, top_p, top_k, tools, tool_choice, structured_outputs) but no reasoning controls at all.
Knowledge cutoff differs too: Scout's is 2024-08-31; M2.5's isn't listed in the catalog. Scout is also about 10 months older (created 2025-04-05 vs M2.5's 2026-02-12), which tracks with the benchmark gap — M2.5 is the newer, purpose-built coding/agent model.
Which model for which job
| Use case | Pick | Why |
|---|---|---|
| Large codebase or document ingestion | Llama 4 Scout | 10,000,000-token context vs M2.5's 204,800 — a 48.83x difference |
| Coding tasks (UI, game dev, data viz) | MiniMax M2.5 | Elo leads of 400+ points across every shared Design Arena category |
| Cost-sensitive bulk input processing | Llama 4 Scout | $1.60 vs $2.16 on a 10M-input/2M-output workload |
| Agentic workflows needing tool-call transparency | MiniMax M2.5 | Mandatory reasoning plus parallel_tool_calls and logprobs support |
| Image-plus-text input tasks | Llama 4 Scout | Only Scout supports image input (text+image->text modality) |
| Long generated output (full files, reports) | MiniMax M2.5 | 196,608 max completion tokens vs Scout's 16,384 |
Questions
- Which model is cheaper for a typical 10M-input, 2M-output job?
- Llama 4 Scout, at $1.60 total on OpenKey pricing versus $2.16 for MiniMax M2.5 — a difference of $0.56 for that workload, driven mostly by M2.5's higher output price of $0.4944/M versus Scout's $0.309/M.
- Does MiniMax M2.5 actually beat Llama 4 Scout on coding?
- Yes, by a wide margin. M2.5 scores 1256 elo in Design Arena's codecategories (rank 29, 56.8% win rate) versus Scout's 839 elo (rank 106, 26.6% win rate) — a 417-point gap in the same benchmark category.
- Which model has a bigger context window?
- Llama 4 Scout, by far — 10,000,000 tokens versus MiniMax M2.5's 204,800 tokens, a context_ratio of 48.83x. If your task needs to process very long inputs in one call, Scout is the only real option here.
- Does either model support image input?
- Only Llama 4 Scout. Its modality is text+image->text, while MiniMax M2.5 is text->text only. If your workload includes screenshots, diagrams, or photos, M2.5 isn't an option regardless of its coding benchmark lead.