A detailed evaluation of Deepseek V4's 1M token context window across production codebases reveals optimal performance at 150-250k tokens, with degradation past 300k and significant latency in reasoning mode. The model exhibits high hallucination rates on unknown tasks, requiring validation layers for production use.
Just ran to verify deepseek v4's context claim of 1M and ran it across three production codebases like 45k (microservice), 180k (monorepo backend) and 520k(full stack app). For the observation, tasks included dependency tracing, cross file refractors and bug isolation to see where recall keeps up **under 150k** Got a solid performance like at 45k tokens, function calls traced across 8 files maintain accurate path reconstruction. At 180k, multi file refractors spanning 14 files show consistent architectural understand and no contradictions or context loss patterns **past 300k** precision quality degrades here. asked for exact line numbers from functions defined 400k tokens earlier, responses give "around line 230" instead of the actual 247. at 520k outputs shift to architectural summaries that skip implementation details, thats a problem if edge cases are a concern **the latency gap** Time to first token measures around 1.19s on deepinfra fp4 endpoint. Time to first answer in max reasoning mode stretches to around 120 seconds since the model completes internal chain of thought before producing visible output, which is really crticial for interative workflows to account for provider benchmarks show 94% hallucination rate on unknown asnwer tasks (aa-omniscience) but v4 generates confident responses without even actual info. Shows up as references to nonexistent utility functions or phantom dependencies on unknown answer tasks v4 generates confident responses without actual grounding, shows up as references to nonexistent utility functions or phantom dependencies. needs a validation layer for anything production critical **practical range** 150-250k tokens appears optimal for coding work. full context retention, sub 2s response latency, minimal precision loss. past 300k requires defensive prompting and source verification. the 1m window functions technically but needs careful handling tho. context size shifts which prompt engineering techniques matter rather than eliminating the need completely
DeepSeek releases V4, a MoE model with a 1M-token context window optimized for agentic tasks through hybrid attention and reduced KV cache requirements.
DeepSeek releases preview versions of its V4 series, including DeepSeek-V4-Pro (1.6T parameters, 49B activated) and DeepSeek-V4-Flash (284B parameters, 13B activated), both supporting a one-million-token context and featuring hybrid attention, manifold-constrained hyper-connections, and a Muon optimizer.
DeepSeek releases V4 series of Mixture-of-Experts language models (Pro 1.6T/49B activated, Flash 284B/13B activated) supporting one-million-token context with hybrid attention and speculative decoding, claiming best open-source model performance.
DeepSeek releases DeepSeek-V4-Flash and DeepSeek-V4-Pro, new MoE language models supporting 1 million token contexts with improved efficiency and performance.