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DeepSeek
DeepSeek R1 Distill Llama 70B
DeepSeek R1 Distill Llama 70B is a reasoning-focused chat model from DeepSeek that distills R1-style chain-of-thought behavior into a 70B-parameter Llama-based architecture.
Input from
$0.800 / 1M tokens
across 3 providers
API Pricing
Cheapest on Novita AI — 33% below avg| Provider | Input / 1M | Output / 1M |
|---|---|---|
| $0.800 | $0.800 | |
| $0.800 | $0.800 | |
| $2.00 | $2.00 |
Prices updated daily. Last check: Sep 5, 2026
DeepSeek R1 Distill Llama 70B pricing by provider
Input, output, and batch rates, plus alternatives, for one provider at a time.
Performance & Benchmarks
Source: Artificial Analysis →Intelligence
4.2 / 100
Math
53.7 / 100
Reasoning & Knowledge
- MMLU-Pro79.5%
- GPQA Diamond40.2%
- Humanity's Last Exam5.1%
Coding
- LiveCodeBench26.6%
Math
- AIME 202553.7%
- AIME67.0%
- MATH-50093.5%
Agentic & Tool Use
- Terminal-Bench Hard1.5%
- τ²-bench21.9%
Instruction & Long Context
- IFBench27.6%
- Long-Context Reasoning10.0%
Benchmarks measured Sep 2026. Scores are independent evaluations, not vendor-reported.
Model Details
General
- Creator
- DeepSeek
- Modalities
- Text
Capabilities
- Tool Calling
- No
- Open Source
- No
Strengths & Limitations
Strengths
- Reasoning-distilled: trained on chain-of-thought traces from DeepSeek R1, so it produces explicit step-by-step derivations
- Built on a 70B dense Llama-family architecture, so existing Llama serving stacks, quantizations, and tooling generally apply
- Open-weight release, enabling self-hosting, fine-tuning, and on-premise deployment
- Dense 70B size fits common multi-GPU inference configurations without MoE routing complexity
- Available from multiple inference providers, giving buyers a choice of latency, throughput, and pricing profiles
- Smaller and cheaper to serve than the full DeepSeek R1 model while retaining R1-style reasoning behavior
Limitations
- Reasoning-style outputs are verbose, increasing output token counts and per-request cost versus a standard instruct model
- Does not match the reasoning ceiling of the full DeepSeek R1 model it was distilled from
- Text-in, text-out chat model — we do not track image or audio input support for this entry
- We do not have a confirmed context window on file; effective limits vary by provider endpoint
- Our throughput and time-to-first-token benchmarks for this model are not populated, so speed must be judged per provider
- Latency is higher than non-reasoning models of similar size because the model generates reasoning tokens before answering
Key Features
•Chain-of-thought reasoning distilled from DeepSeek R1
•70B-parameter dense Llama-based architecture
•Open weights available for self-hosting and fine-tuning
•Text chat completion interface across multiple hosted providers
•Multi-step math, logic, and code-reasoning orientation
•Compatible with standard Llama-family quantization and serving stacks (vLLM, SGLang, llama.cpp ecosystems)
•Part of the DeepSeek R1 distill family, alongside smaller Qwen-based variants
About DeepSeek R1 Distill Llama 70B
DeepSeek R1 Distill Llama 70B is part of DeepSeek's R1 distillation series, in which reasoning traces produced by the larger DeepSeek R1 model are used to fine-tune existing open dense models. As the name indicates, this entry uses a 70B-parameter Llama-family base rather than DeepSeek's own MoE architecture, making it a distilled sibling of R1 rather than a scaled-down version of the same base network. Within the R1 distill lineup it sits at the larger end, alongside smaller Qwen-based distills.
The model is a text chat model that emits explicit reasoning before a final answer, a behavior inherited from R1's training signal. That style tends to help on multi-step math, logic, and code-reasoning tasks, at the cost of longer outputs and higher token consumption per request than a comparable non-reasoning 70B instruct model. Because it is served by multiple third-party inference providers, throughput, maximum context length, and reasoning-token handling can differ between endpoints — we do not track a single confirmed context window for this model, so check the provider's own documentation before committing to a long-context workload.
In practice, teams pick R1 Distill Llama 70B when they want R1-like step-by-step reasoning in a dense 70B model that can be self-hosted on commodity multi-GPU nodes or rented from several competing API providers. Compared with full DeepSeek R1, it is smaller and cheaper to run but does not match the parent model's reasoning ceiling; compared with a standard Llama 70B instruct model, it trades verbosity and latency for stronger structured reasoning.
Common Use Cases
This model suits workloads where the quality of intermediate reasoning matters more than raw response speed: competition-style math problems, algorithmic and debugging questions, logic puzzles, exam-style question answering, and generating explanations or worked solutions for education tools. It is also used to produce synthetic reasoning data for training smaller models, and as a self-hosted reasoning option for teams that need weights on their own infrastructure for compliance or cost-control reasons. It is a weaker fit for latency-sensitive chat UIs, high-volume classification or extraction jobs, and short-answer tasks where the extra reasoning tokens are pure overhead — a smaller non-reasoning instruct model usually serves those better.
Frequently Asked Questions
How much does DeepSeek R1 Distill Llama 70B cost to use?
Pricing varies by provider and by pricing type — hosted APIs bill separately for input and output tokens, and reasoning models consume more output tokens than usual, while self-hosting shifts the cost to GPU rental. Check the pricing table on this page for current per-provider rates, and factor in the longer outputs this model generates.
What is DeepSeek R1 Distill Llama 70B best used for?
Multi-step reasoning tasks: math problem solving, code reasoning and debugging, logic-heavy question answering, and generating worked explanations. It is also used to create reasoning-style synthetic training data.
How does it differ from the full DeepSeek R1?
Full R1 is DeepSeek's own large mixture-of-experts reasoning model. This entry is a 70B dense Llama-based model fine-tuned on reasoning traces generated by R1, so it is cheaper and simpler to serve but does not reach the parent model's reasoning quality.
Is this the same as Llama 70B?
No. It uses a Llama-family 70B base architecture, but DeepSeek fine-tuned it on R1 reasoning data, which changes its output style — it produces explicit step-by-step reasoning before final answers rather than replying directly like a standard Llama instruct model.
Can I run it on my own hardware?
Yes — the R1 distill weights were released openly, and because the architecture is Llama-family it works with common inference servers and quantization formats. A 70B dense model typically needs multiple GPUs at higher precisions, or fewer with aggressive quantization.
How fast is it?
Our benchmark record for output tokens per second and time to first token is not populated for this model, so speed depends on the provider and hardware you choose. Expect longer end-to-end response times than non-reasoning models of similar size because reasoning tokens are generated before the final answer.