Throughput (tokens / sec)
avg. 150tokens/s
High-throughput agentic work with 1M context
Run instantly. Pay only for what you use.
DeepSeek V4 Flash is DeepSeek's efficiency-focused open-weight model: a 284B-parameter MoE that activates just 13B per token, with a 1M-token context window and MIT-licensed weights. Three thinking levels — non-think, think-high, and think-max — let you pick speed or reasoning depth per request.
Its hybrid sparse attention is built for long context: at 1M tokens it runs on roughly 27% of the compute and 10% of the KV cache of DeepSeek V3.2, so whole-repo prompts and heavily cached agent context stay fast and affordable.
The result is production-grade agentic coding at a small-model price: 79% on SWE-bench Verified and 91.6 on LiveCodeBench in max-thinking mode, with strict function calling and JSON output available in every mode.
1Mtoken context window
Sparse attention runs 1M-token prompts on ~27% of the compute of DeepSeek V3.2.
13Bactive of 284B total
MoE routing keeps per-request cost and latency near small-model levels.
3thinking levels
Non-think, think-high, and think-max trade latency for reasoning depth per request.
DeepSeek V4 Flash is strongest where agents reason over a lot of context: coding loops, tool chains, and document pipelines that need long memory without dense-model cost.
Holds a plan across long tool-call chains — the 0731 re-post-train pushes Terminal-Bench 2.1 to 82.7, up from 61.8 on the preview build.
Non-think, think-high, and think-max — the 0731 re-post-train sharpens the reasoning modes hardest (DeepSWE 7.3 → 54.4), so you pay for depth only where a request needs it.
Hybrid sparse attention reads 1M tokens with ~10% of the KV cache of V3.2 — whole repos and document sets in one prompt.
Strict function calling and JSON mode in every thinking level — tool arguments and typed pipelines that parse the first time.
Long-running coding agents
Plans, edits, and verifies across long tool-call chains — 79% on SWE-bench Verified in max-thinking mode.
Whole-repo and long-document analysis
The 1M-token window holds a full repository or document set in one prompt, and sparse attention keeps it affordable.
High-volume, cost-sensitive fleets
13B active parameters and deep cache-read discounts keep per-request cost flat as traffic scales.
Structured extraction to JSON
Strict function calling and JSON mode return schema-valid output for pipelines — in every thinking mode.
Image, audio, or video inputs
DeepSeek V4 Flash is text-only. Route visual inputs to a multimodal model such as Gemma 4 31B or Qwen 3.6 35B-A3B.
Fact recall without retrieval
Like other small-activation models it is weak on standalone knowledge recall (34.1% on SimpleQA-Verified) — pair it with RAG for factual workloads.
Tight output-token budgets in think-max
Max thinking emits long reasoning traces that drive up output tokens and latency. Use non-think or think-high for cost- and latency-sensitive replies.
Compare Entrim’s pricing, powered by an optimized inference runtime, against other providers using the same token counts per request.
avg. 150tokens/s
avg. 820ms
82.7Terminal-Bench 2.1
Agentic coding and shell tasks run end-to-end in a real terminal.
54.4DeepSWE
Resolves real software-engineering issues across long tool-call chains.
70.3Toolathlon (verified)
Sustains long multi-tool workflows without losing the plan.
Code examples
curl -X POST "https://api.entrim.ai/v1/chat/completions" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer $ENTRIM_API_KEY" \ -d '{ "model": "deepseek-ai/DeepSeek-V4-Flash", "messages": [ { "role": "user", "content": "Why do they call it a building if it is already built?" } ] }'SDKs & docs
Use familiar SDK patterns with quickstart examples for common production setups.
OpenAI-compatible
Keep the request format your team already knows.
No cold starts
Production requests are served without model spin-up delays.
Tool-heavy coding at 3B-active cost
o4-mini-class reasoning for tool-driven coding
Multimodal long-context work with thinking control