Model profile
DeepSeek

DeepSeek: R1

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

Best for: Coding / Long-context researchN/A latencyStandard contextBudget pricing
Intelligence
20.4

Benchmark blend

Coding
0.770

Dev workflow signal

Context
64K Tokens

Standard

Input Price
$1.35

Budget tier

Decision snapshot
53

DeepSeek: R1 currently reads as a budget text-first option with standard context and a partially published runtime profile.

Overall profile
Use-case specific
Best for
Coding / Long-context research
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
20.4
20

General reasoning and benchmark headroom.

Limited
Speed
N/A
N/A

Latency data is partial.

Unavailable
Context
64K Tokens
64

How much prompt and task state can stay in view.

Competitive
Price
$1.35
86

$3.00 output / 1M

Efficient

Editorial Profile

DeepSeek: R1 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 77Math score 76

DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....

Identity

DeepSeek text-first profile

Positioning

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

Cost posture

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

Strengths
  • Coding indicators point to a strong developer workflow fit.

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
  • Code generation, refactors, test writing, and tool-assisted debugging.

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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
20.4
MMLU Pro
84.9%
GPQA
81.3%
HLE
15.8%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.770
SciCode
40.3%
Math

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

Math Index
76.0
AIME
89.3%
AIME 2025
76.0%
Math 500
98.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.6%
TAU2
36.5%
TerminalBench Hard
15.9%
LCR
56.7%

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
64K Tokens
Vision
Text-first
Modalities
text
Tokenizer
DeepSeek
Max Completion
16000
Moderation
No
Supported Parameters
frequency_penaltyinclude_reasoningmax_tokenspresence_penaltyreasoningrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Input Modalities
text
Output Modalities
text
Price architecture
Input
per 1M input tokens
$1.35
Output
per 1M output tokens
$3.00
Blended
AA 3:1 mix
$1.76

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.