LLM Reference

DeepSeek Coder V2 Lite

Released
2024-06-17
Last refreshed
2026-04-15
Status
Researched 163d ago
Open WeightsCommercial use allowedCodingLong contextClassification

DeepSeek Coder V2 Lite is worth evaluating for coding, long context, and classification when its provider route and context window match the workload.

Use it for

  • Teams evaluating coding, long context, and classification
  • Workloads that can use a 128k context window
  • Buyers comparing 1 tracked provider route

Do not use it for

  • Vision or document-understanding workloads
  • Strict JSON or tool-calling flows
Specifications
Released
2024-06-17
Context
128k
Parameters
16B
Architecture
Mixture of Experts
Knowledge cutoff
2023-11
Specialization
code
Openness
Open weights
License
DeepSeek LicenseCommercial use allowed
Training
finetuned
Fine-tuning
base
Created by

Advancing artificial general intelligence (AGI).

Hangzhou, Zhejiang, China
Founded 2023
Website
Pricing
Output / 1M
$0.500
Input / 1M
$0.500

Cheapest of 1 route · Fireworks AI

About

DeepSeek Coder V2 Lite is an open-source Mixture-of-Experts (MoE) language model specifically tailored for efficiency and cost-effectiveness in coding tasks. It operates with a 15.7B parameter count, but only 2.4B are active at any given time, making it comparable to GPT4-Turbo for code-centric applications. This model supports 338 programming languages and has an extended context length of 128K tokens, facilitating the handling of complex codebases and lengthy prompts. Its features encompass code generation, completion, understanding, and mathematical reasoning, making it versatile for diverse coding applications. Available on Hugging Face, Ollama, and other platforms, DeepSeek Coder V2 Lite offers accessible solutions for developers and researchers, with performance that rivals or surpasses some closed-source models.

DeepSeek Coder V2 Lite is an open-weight model in the DeepSeek Coder V2 family. The structured metadata tracks a 128k-token context window. This page tracks provider routes through Fireworks AI, with the cheapest tracked route listed at $0.5 input and $0.5 output per 1M tokens. Headline tracked benchmarks include Google-Proof Q&A 52.4, HellaSwag 91.4, and HumanEval 81.1.

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

Coding

Q/$ B

1 relevant benchmark in the decision map.

Long context

Included by capability and metadata signals in the decision map.

Classification

Q/$ C

2 relevant benchmarks in the decision map.

Provider price ladder

Compare API pricing across 1 providers for input and output tokens, batch, and cached reads when available.

ProviderInput / 1MOutput / 1MRoute
Fireworks AI$0.500$0.500
ServerlessProvisioned

Available via routers & gateways(1)

Capabilities

No model capability flags are currently sourced.

Benchmark peer barsfor Coding

Benchmark scores(4)

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&A52.4diamondhttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
HellaSwag91.410-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
HumanEval81.1pass@1https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard
Massive Multitask Language Understanding78.95-shothttps://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard

Migration checks

No linked migration route is available for this model yet.