Model profile
DeepSeek

DeepSeek: R1 Distill Llama 70B

DeepSeek: R1 Distill Llama 70B is a budget-priced text-first model from DeepSeek with partial runtime data, compact context posture, and the clearest fit around long-context research / coding.

Best for: Long-context research / CodingN/A latencyCompact contextBudget pricing
Intelligence
9.8

Benchmark blend

Coding
0.266

Dev workflow signal

Context
8K Tokens

Compact

Input Price
$0.70

Budget tier

Decision snapshot
31

DeepSeek: R1 Distill Llama 70B currently reads as a budget text-first option with compact context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Coding
Latency tier
N/A
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
9.8
10

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
8K Tokens
28

How much prompt and task state can stay in view.

Limited
Price
$0.70
86

$1.10 output / 1M

Efficient

Editorial Profile

DeepSeek: R1 Distill Llama 70B in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 27Math score 54

DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...

Identity

DeepSeek text-first profile

Positioning

Long-context research / Coding with compact context and partially published runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency data is incomplete, so interactive responsiveness is harder to rank confidently.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context window is more comfortable for focused tasks than extremely long sessions.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
9.8
MMLU Pro
79.5%
GPQA
40.2%
HLE
5.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.266
SciCode
31.3%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
53.7
AIME
67.0%
AIME 2025
53.7%
Math 500
93.5%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
27.6%
TAU2
21.9%
TerminalBench Hard
1.5%
LCR
9.3%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
8K Tokens
Vision
Text-first
Modalities
text
Tokenizer
Llama3
Max Completion
8192
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningrepetition_penaltyseedstoptemperaturetop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$0.70
Output
per 1M output tokens
$1.10
Blended
AA 3:1 mix
$0.80

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.