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Put up to 4 models beside each other — token prices, context windows, capabilities and provider, from the same catalogue the model pages read.

  1. DeepSeek V4.1 FlashDeepSeekRemove
  2. Codestral 2508Mistral AIRemove
  3. Muse Spark 1.3MetaRemove
deepseek-v4.1-flash vs codestral-2508 vs muse-spark-1.3
AttributeDeepSeek V4.1 Flashdeepseek-v4.1-flashCodestral 2508codestral-2508Muse Spark 1.3muse-spark-1.3
Pricing
Input$0.30 / 1M$0.45 / 1M$1.25 / 1M
Output$1.20 / 1M$1.35 / 1M$4.25 / 1M
Cache Write (5m)$0.30 / 1M$0.45 / 1M$1.25 / 1M
Cache Write (1h)$0.30 / 1M$0.45 / 1M$1.25 / 1M
Cache Read$0.30 / 1M$0.45 / 1M$1.25 / 1M
Web Search$0 / 1M$0 / 1M$0 / 1M
Context
Max context1M256K1M
Max outputN/AN/AN/A
Capabilities
VisionYesNoYes
Function CallingYesYesYes
JSON ModeYesYesYes
StreamingYesYesYes
Catalogue
ProviderDeepSeekMistral AIMeta
Categorychatchatchat
Charge typePay As You GoPay As You GoPay As You Go
Released
Description
SummaryDeepSeek V4.1 Flash is a cost-efficient sparse Mixture-of-Experts (MoE) model in DeepSeek's V4.1 family, optimized for coding, reasoning, and agentic workflows. Despite its efficiency-focused positioning, DeepSeek reports that it surpasses the previous V4 Pro in performance, inference speed, and overall task completion time. The model is particularly strong at long-horizon, multi-step execution, making it well suited for coding agents, complex problem solving, and autonomous workflows that must reliably carry tasks through to completion.Codestral is Mistral's cutting-edge language model for coding, released in late July 2025. It is purpose-built for low-latency, high-frequency developer workflows, excelling at tasks such as fill-in-the-middle (FIM) code completion, code correction, and test generation. Optimized for responsiveness and precision, Codestral is well suited for real-time coding assistance, IDE integration, and automated development pipelines where speed and accuracy are critical.Muse Spark 1.3 is Meta's multimodal reasoning model designed for long-running agentic, multi-agent, and coding workflows. It maintains context and information across extended tasks, enabling reliable execution in complex, multi-step environments. The model is optimized to resolve conflicting information, seek clarification or confirmation when necessary, and execute concisely, making it well suited for autonomous agents, collaborative multi-agent systems, and long-horizon software engineering workflows.