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 | Olmo 3.1 32B Instructolmo-3.1-32b-instruct | Kimi K3.1 Prokimi-k3.1-pro | Gemini 4 Argongemini-4-argon |
|---|---|---|---|
| Pricing | |||
| Input | $0.20 / 1M | $2.00 / 1M | $2.00 / 1M |
| Output | $0.60 / 1M | $10.00 / 1M | $10.00 / 1M |
| Cache Write (5m) | $0.20 / 1M | $2.00 / 1M | $2.00 / 1M |
| Cache Write (1h) | $0.20 / 1M | $2.00 / 1M | $2.00 / 1M |
| Cache Read | $0.20 / 1M | $2.00 / 1M | $2.00 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 65.5K | 1M | 1M |
| Max output | N/A | N/A | N/A |
| Capabilities | |||
| Vision | No | Yes | Yes |
| Function Calling | Yes | Yes | Yes |
| JSON Mode | Yes | Yes | Yes |
| Streaming | Yes | Yes | Yes |
| Catalogue | |||
| Provider | Allen AI | MoonShot AI | |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Olmo 3.1 32B Instruct is a 32B-parameter instruction-tuned model optimized for conversational AI and multi-turn dialogue. It focuses on strong instruction following and responsive chat behavior while maintaining solid reasoning and coding performance. Released by AI2 under Apache 2.0, it is fully open and transparent. | Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model, building on Kimi K3 with enhanced post-training for advanced reasoning and autonomous task execution. It features a 1M-token context window and native image and video understanding, enabling complex, long-context multimodal workflows. Optimized for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, Kimi K3.1 Pro is well suited for autonomous software engineering, multimodal analysis, and complex professional workflows that require sustained reasoning and reliable execution. | Gemini 4 Argon is Google's flagship reasoning model, built for complex software engineering, deep mathematical and scientific problem solving, and autonomous multi-step agents. Google trained it to keep context across long, multi-step tasks and to turn long documents mixing text, tables, charts and images into a single grounded output. It leads the Vals Index across finance, legal, coding and tax work, and supports long decode of up to 1M output tokens in a single response. |