Llama 4 Scout wins on context window (512K vs 128K). Mistral Small 3.1 wins on coding (55 vs 54) and writing quality and price ($0.1 vs $0.5/1M input). For most workflows, Mistral Small 3.1 is the stronger default — ultra-cheap multimodal model for massive-volume, low-complexity pipelines.
The shortest way to see the safest default, the lower-cost option, and the specialist pick before you read deeper.
Mistral Small 3.1 is the safest overall answer here when you want the strongest default instead of the lowest list price.
Switch the scoring lens to see whether the top answer changes when you care more about cost, speed, or long-document work.
Meta / Budget / Mar 27, 2026
Best open-weight long-context option for self-hosted pipelines.
Ranks models by the broadest mix of coding, writing, research, and long-context usefulness.
You want a hosted solution — Gemini 3.1 Flash gives more context for roughly the same cost.
The fastest way to see where the recommendation shifts when your priority changes.
One of the cheapest models in the directory at $0.10/1M input
Multimodal — handles images alongside text at this price point
Fast and efficient for simple, well-defined tasks
Weak on complex reasoning, hard coding, and nuanced writing
Not suitable for tasks requiring deep context retention or multi-step logic
UseRightAI recommendations are based on practical decision factors people actually feel in day-to-day use.
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Mistral Small 3.1 wins on more categories — writing, budget, multimodal. Llama 4 Scout is the better pick when affordable self-hosted long-context workflows and analysis pipelines. The right choice depends on your specific use case.
Mistral Small 3.1 is cheaper at $0.1/1M input and $0.3/1M output. Llama 4 Scout costs $0.5/1M input and $1.2/1M output.
Llama 4 Scout has the larger context window at 512K tokens vs Mistral Small 3.1's 128K. For large document analysis, Llama 4 Scout is the stronger pick.
Mistral Small 3.1 is better for coding with a score of 55 vs Llama 4 Scout's 54. For the highest coding quality available, Claude Sonnet 4.6 (79.6% SWE-bench) or Opus 4.6 (80.8%) remain benchmarks.
Mistral Small 3.1 is faster with a very fast speed rating (score: 5) vs Llama 4 Scout's fast rating (score: 4).
Meta: Llama 3.1 8B Instruct is the lower-cost option to start with when you still need useful output at scale.
Llama 4 Scout is the better pick when response speed matters more than maximum reasoning depth.
Mistral Small 3.1 leads on coding with a score of 55 vs 54 for Llama 4 Scout.
Llama 4 Scout has the larger context window: 512K vs 128K for Mistral Small 3.1.
Mistral Small 3.1 is cheaper at $0.1/1M input tokens vs $0.5/1M for Llama 4 Scout.
Choose Mistral Small 3.1 for writing and budget — ultra-high-volume classification.
Choose Llama 4 Scout when affordable self-hosted long-context workflows and analysis pipelines.
Both models serve different primary workflows — consider using each where it has a clear edge.
Limited to simpler use cases compared to Codestral or DeepSeek V3
