LLM Reference

DeepSeek V4 Flash

Released
2026-04-24
Last refreshed
2026-06-01
Status
Researched 7d ago
Open SourceCommercial use allowedCodingRAGAgentsLong contextClassificationJSON / Tool use

DeepSeek V4 Flash is worth evaluating for coding, rag, and agents when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, rag, and agents
  • Workloads that can use a 1m context window
  • Buyers comparing 4 tracked provider routes

Do not use it for

  • Vision or document-understanding workloads
Specifications
Released
2026-04-24
Context
1m
Max output
384,000
Parameters
284B
Architecture
Mixture of Experts
Specialization
general
Openness
Open source
License
MIT(OSI)Commercial use allowed
Training
pretrained
Created by

Advancing artificial general intelligence (AGI).

Hangzhou, Zhejiang, China
Founded 2023
Website
Pricing
Output / 1M
$0.1966
Input / 1M
$0.0983

Cheapest of 5 routes · OpenRouter

About

DeepSeek V4 Flash is a 284B parameter (13B activated) Mixture-of-Experts language model with 1M-token context. Features a hybrid attention architecture combining Compressed Sparse Attention (CSA) and Heavily Compressed Attention (HCA) for efficient long-context inference. Supports thinking and non-thinking modes. Legacy API aliases deepseek-chat and deepseek-reasoner map to this model's non-thinking and thinking modes respectively. Pricing: $0.14/1M input, $0.28/1M output (cache hit: $0.0028/1M input). MIT licensed.

DeepSeek V4 Flash is an open-source model in the DeepSeek V4 family. The structured metadata tracks a 1m-token context window, reasoning, function calling, tool use, and structured outputs. This page tracks provider routes through DeepSeek Platform, OpenRouter, Microsoft Foundry, and 2 more, with the cheapest tracked route listed at $0.0983 input and $0.1966 output per 1M tokens. Headline tracked benchmarks include Google-Proof Q&A 88.1, MMLU PRO 86.2, and SWE-bench Verified 79.0.

Top use-case fit: coding, agents, and build tasks

Coding

Q/$ B

4 relevant benchmarks in the decision map.

RAG

Included by capability and metadata signals in the decision map.

Agents

Q/$ A

1 relevant benchmark in the decision map.

Provider price ladder

Compare all 5

Compare API pricing across 4 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MCacheRoute
OpenRouter$0.0983$0.1966-
Serverless
DeepSeek Platform$0.140$0.280read $0.0028
Serverless
Novita AI$0.140$0.280-
Serverless
Vercel AI Gateway$0.140$0.280read $0.0028
Serverless

Available via routers & gateways(8)

Capabilities

ReasoningFunction CallingTool UseStructured Outputs

Benchmark peer barsfor Coding

Benchmark scores(8)

Scores are benchmark-specific and are direction-aware: the same numeric gap can mean very different outcomes across suites. Use the leaderboard context and this model's provider route to decide whether the winning margin is meaningful for your workload.
BenchmarkScoreVersionSource
Google-Proof Q&A88.1diamondhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
MMLU PRO86.2Think Maxhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
SWE-bench Verified79.0Think Maxhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
SWE-bench Pro52.6Think Maxhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
LiveCodeBench91.6Think Maxhttps://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
HumanEval69.5Base model non-think mode (pass@1)https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
Massive Multitask Language Understanding88.7Base model (DeepSeek-V4-Flash-Base) (accuracy)https://huggingface.co/deepseek-ai/DeepSeek-V4-Flash
Terminal-Bench 2.056.9Terminal-Bench 2.0 (accuracy%)https://benchlm.ai/benchmarks/terminalBench2

Migration checks

No linked migration route is available for this model yet.

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