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.
| Attribute | Muse Spark Openmuse-spark-open | Olmo 3 7B Thinkolmo-3-7b-think | DeepSeek V4.1 Flash 1017deepseek-v4.1-flash-1017 |
|---|---|---|---|
| Pricing | |||
| Input | $1.25 / 1M | $0.12 / 1M | $0.10 / 1M |
| Output | $4.25 / 1M | $0.20 / 1M | $0.50 / 1M |
| Cache Write (5m) | $1.25 / 1M | $0.12 / 1M | $0.10 / 1M |
| Cache Write (1h) | $1.25 / 1M | $0.12 / 1M | $0.10 / 1M |
| Cache Read | $1.25 / 1M | $0.12 / 1M | $0.10 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 1M | 65.5K | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | No | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Meta | Allen AI | DeepSeek |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Muse Spark Open is an open-weight, coding-optimized model designed for agentic software engineering workflows. It delivers strong first-attempt accuracy, reliable tool calling, and deep codebase understanding, enabling effective development across real-world repositories. Optimized for autonomous coding agents, complex code navigation, and tool-driven development, Muse Spark Open is well suited for software engineering automation, debugging, code generation, and multi-step coding workflows where accuracy and reliable execution are essential. | Olmo 3 7B Think is a lightweight reasoning-focused model in the Olmo family, aimed at multi-step problem solving, logical inference, and instruction-driven tasks while keeping conversations coherent. Built by AI2 and released under Apache 2.0, it's fully open and well suited for both research and practical NLP workflows. | DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek’s cost-efficient Mixture-of-Experts (MoE) model, built on its innovative Causal Encoder-Decoder (CED) architecture. Featuring 552B total parameters with 8B activated for input and 16B for output, it combines efficient inference with native multimodal understanding through jointly trained text and visual representations. Optimized for coding, terminal and computer-use agents, and long-horizon workflows, the model introduces compressed KV caching that reduces cache memory usage to approximately one-quarter of the previous Flash generation. DeepSeek reports that it surpasses V4 Pro in performance, inference speed, and task completion time, making it well suited for cost-efficient autonomous agents, complex software engineering, and long-context analysis. |