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DeepSeek added deepseek-v4-pro and deepseek-v4-flash to its API model catalog on April 24, 2026. Both models support thinking and non-thinking modes, 1M token context, 384K max output, JSON output, tool calls, and FIM in non-thinking mode. Official pricing lists V4 Flash at $0.14 cache-miss input / $0.028 cache-hit input / $0.28 output per 1M tokens, and V4 Pro at $1.74 cache-miss input / $0.145 cache-hit input / $3.48 output per 1M tokens.
| Field | Before | After |
|---|---|---|
| models | deepseek-chat, deepseek-reasoner | deepseek-v4-flash, deepseek-v4-pro |
| compatibility_aliases | — | { "deepseek-chat": "deepseek-v4-flash non-thinking mode", "deepseek-reasoner": "deepseek-v4-flash thinking mode" } |
| thinking_mode | — | { "default": "enabled", "openai_format": { "thinking": { "type": "enabled/disabled" }, "reasoning_effort": "high/max" }, "anthropic_format": { "output_config": { "effort": "high/max" } } } |
| context_window | 128000 | 1000000 |
| max_output | — | 384000 |
| features | — | json_output, tool_calls, chat_prefix_completion, fim_non_thinking |
| api_pricing | — | { "deepseek-v4-flash": { "cache_hit_input_per_1m": 0.028, "cache_miss_input_per_1m": 0.14, "output_per_1m": 0.28 }, "deepseek-v4-pro": { "cache_hit_input_per_1m": 0.145, "cache_miss_input_per_1m": 1.74, "output_per_1m": 3.48 } } |
Add explicit routing entries for deepseek-v4-pro and deepseek-v4-flash instead of relying only on deepseek-chat or deepseek-reasoner aliases.
Benchmark V4 Pro with reasoning_effort=max for agentic coding and complex reasoning before promoting it to a frontier route.
For thinking-mode tool calls, store assistant reasoning_content with tool_calls and pass it back in subsequent requests.
Update cost dashboards to distinguish cache-hit input, cache-miss input, and output tokens for both V4 tiers.
Official DeepSeek social announcement URL for the V4 Pro and V4 Flash launch.
DeepSeek lists deepseek-v4-flash and deepseek-v4-pro with 1M context, 384K max output, cache-hit input, cache-miss input, and output token pricing.
DeepSeek documents thinking mode toggles, reasoning effort controls, reasoning_content behavior, and tool-call handling for deepseek-v4-pro.
DeepSeek describes V4 Pro and V4 Flash as MoE models with 1M token context and configurable non-think, high, and max reasoning modes.
Check your models and usage for free. See the relevant official changes, estimated cost, and a practical next step.