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 | Ministral 3 8B 2512ministral-8b-2512 | GLM 5.3glm-5.3 | DeepSeek V4.1 Flashdeepseek-v4.1-flash |
|---|---|---|---|
| Pricing | |||
| Input | $0.15 / 1M | $1.40 / 1M | $0.30 / 1M |
| Output | $0.15 / 1M | $4.40 / 1M | $1.20 / 1M |
| Cache Write (5m) | $0.15 / 1M | $1.40 / 1M | $0.30 / 1M |
| Cache Write (1h) | $0.15 / 1M | $1.40 / 1M | $0.30 / 1M |
| Cache Read | $0.15 / 1M | $1.40 / 1M | $0.30 / 1M |
| Web Search | $0 / 1M | $0 / 1M | $0 / 1M |
| Context | |||
| Max context | 262.1K | 1M | 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 | Mistral AI | Z.AI | DeepSeek |
| Category | chat | chat | chat |
| Charge type | Pay As You Go | Pay As You Go | Pay As You Go |
| Released | — | — | — |
| Description | |||
| Summary | Ministral 3 8B is the balanced model in the Ministral 3 series — a compact, efficient language model that still delivers strong performance and includes vision capabilities. | GLM-5.3 is Z.ai's large-scale reasoning model designed for complex software engineering and long-horizon agentic workflows. It supports text input and output with a 1M-token context window, enabling sustained reasoning across large codebases and extended multi-step tasks. Building on GLM-5.2, it delivers stronger coding performance while improving the balance between capability and token efficiency, making it well suited for autonomous coding agents, large-scale engineering workflows, and complex task execution. | 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. |