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 | Gemma 4 31B (Free)gemma-4-31b-it:free | Gemini 3.8 Flashgemini-3.8-flash | Muse Spark 1.3muse-spark-1.3 |
|---|---|---|---|
| Pricing | |||
| Input | $0 / 1M | $0.75 / 1M | $1.25 / 1M |
| Output | $0 / 1M | $3.75 / 1M | $4.25 / 1M |
| Cache Write | $0 / 1M | — | — |
| Cache Read | $0 / 1M | $0.75 / 1M | $1.25 / 1M |
| Cache Write (5m) | — | $0.75 / 1M | $1.25 / 1M |
| Cache Write (1h) | — | $0.75 / 1M | $1.25 / 1M |
| Web Search | — | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 262.1K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | Yes | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Meta | ||
| Category | chat | chat | chat |
| Charge type | Free | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model, supporting text and image inputs with text outputs. It features a 256K token context window, configurable thinking/reasoning modes, native function calling, and broad multilingual support across 140+ languages. The model delivers strong performance in coding, reasoning, and document understanding, making it well suited for developer workflows, multilingual applications, and structured knowledge tasks. | Gemini 3.8 Flash is Google's most intelligent Flash-class model, delivering significant improvements over Gemini 3.7 Flash across software engineering, agentic workflows, and complex multi-step reasoning. Designed to combine strong capability with Flash-tier efficiency, it is well suited for coding assistants, autonomous agents, and high-throughput production workflows that require responsive performance without sacrificing reasoning quality. | 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. |