Compare models

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

DeepSeek V4.1 FlashDeepSeek Remove
Perceptron Mk1.5Perceptron Remove
Qwen3 ASR 0.6BQwen Remove
Muse Spark 1.3Meta Remove

4 is the maximum. Remove one to add another.

deepseek-v4.1-flash vs perceptron-mk1.5 vs qwen3-asr-0.6b vs muse-spark-1.3
Attribute	DeepSeek V4.1 Flashdeepseek-v4.1-flash	Perceptron Mk1.5perceptron-mk1.5	Qwen3 ASR 0.6Bqwen3-asr-0.6b	Muse Spark 1.3muse-spark-1.3
Pricing
Input	$0.30 / 1M	$0.15 / 1M	$0 / 1M	$1.25 / 1M
Output	$1.20 / 1M	$1.50 / 1M	$0 / 1M	$4.25 / 1M
Cache Write (5m)	$0.30 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Cache Write (1h)	$0.30 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Cache Read	$0.30 / 1M	$0.15 / 1M	Not applicable	$1.25 / 1M
Web Search	$0 / 1M	$0 / 1M	$0 / 1M	$0 / 1M
Context
Max context	1M	36.9K	N/A	1M
Max output	N/A	N/A	N/A	N/A
Capabilities
Vision	Yes	No	No	Yes
Function Calling	Yes	No	No	Yes
JSON Mode	Yes	No	No	Yes
Streaming	Yes	Yes	No	Yes
Catalogue
Provider	DeepSeek	Perceptron	Qwen	Meta
Category	chat	chat	voice	chat
Charge type	Pay As You Go	Pay As You Go	Pay As You Go	Pay As You Go
Released	—	2026-09-25	2026-08-13	—
Description
Summary	DeepSeek 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.	Perceptron Mk1.5 chat model that accepts audio input. Audio and text input are billed per token.	Qwen3-ASR 0.6B speech-to-text. Billed per second of audio.	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.