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Llama 3.1 70B Instruct vs R1

vs
R1

DeepSeek

74#139
Signal-by-Signal Comparison
SignalLlama 3.1 70B InstructDeltaR1
Capabilities
50
-17
67
Benchmarks
72
-1
73
Pricing
100
+2
98
Context window size
81
+5
76
Recency
0
-22
22
Output Capacity
67
+0
67
Overall Result
3 wins
of 6
3 wins
It's a tie - both models win 3 signals each

Score History

Score History (32 data points)
Llama 3.1 70B InstructR1
Llama 3.1 70B Instruct

65.3

current score

Leader

R1

right now

R1

73.8

current score

LMMarketCap.com
Interactive Price Comparison
100Kcalls/month
1,000tokens (~1,333 chars)
500tokens (~667 chars)

Llama 3.1 70B Instruct

Meta

Best Value
Per request$0.000600
Daily$2.00
Monthly$60.00
Annual$720.00

R1

DeepSeek

Per request$0.001950
Daily$6.50
Monthly$195.00
Annual$2340.00

Llama 3.1 70B Instruct saves you $135.00/month

That's $1620.00/year compared to R1 at your current usage level of 100K calls/month.

69% cheaper
Choose Llama 3.1 70B Instruct for cost optimization

Llama 3.1 70B Instruct pricing:
Input:$0.40/M tokens
Output:$0.40/M tokens
R1 pricing:
Input:$0.70/M tokens
Output:$2.50/M tokens
Llama 3.1 70B Instruct

Meta

65

Composite Score

Winner
R1

DeepSeek

74

Composite Score

Signal-by-Signal Comparison
MetricLlama 3.1 70B InstructR1Winner
Overall Score
65
74
R1
Rank#196#139
R1
Quality Rank#196#139
R1
Adoption Rank#196#139
R1
Parameters70B----
Context Window131K64K
Llama 3.1 70B Instruct
Pricing$0.40/$0.40/M$0.70/$2.50/M--
Signal Scores
Capabilities
50
67
R1
Benchmarks
72
73
R1
Pricing
100
98
Llama 3.1 70B Instruct
Context window size
81
76
Llama 3.1 70B Instruct
Recency
0
22
R1
Output Capacity
67
67
Llama 3.1 70B Instruct
Benchmark Head-to-Head(15 benchmarks)
Llama 3.1: 2R1: 7
Llama 3.1
R1
Normalized 0-100%
MMLU
86%90.8%
MMLU-Pro
62.8%84%
GPQA Diamond
46.7%71.5%
MATH-500
68%97.3%
HumanEval
80.5%-
SWE-bench Verified
-49.2%
AIME 2024
-79.8%
GSM8K
95.1%-
IFEval
83.6%83.3%
BBH
81.2%85%
ARC-Challenge
94.8%-
HellaSwag
94.8%-
Arena Elo
11981369
LiveBench
53.3%67.3%
BigCodeBench
46.1%29.7%
Benchmark Interpretation

Our score (0-100) is driven by benchmark performance (90%) from Arena Elo ratings, MMLU, GPQA, HumanEval, SWE-bench, and 15+ standardized evaluations. Capabilities and context window serve as tiebreakers (10%). Learn more about our methodology.

Llama 3.1 70B InstructCompetitive

Scores 65/100 (rank #196), placing it in the top 33% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100
R1Strong Performer

Scores 74/100 (rank #139), placing it in the top 52% of all 290 models tracked.

Raw Quality0/100
Cost Efficiency0/100
Speed0/100

R1 has a 9-point advantage, which typically translates to noticeably better performance on complex reasoning, code generation, and multi-step tasks.

When to Use Each Model

Choose Llama 3.1 70B Instruct when you need:

  • High-volume production workloads where API costs must be minimized
  • Processing long documents or large codebases (131K token context)
  • Self-hosted deployments where you need full control over the model

Choose R1 when you need:

  • Step-by-step reasoning and chain-of-thought problem solving
  • Self-hosted deployments where you need full control over the model
Cost-Performance Analysis
Llama 3.1 70B InstructBest Value
Input cost$0.40/M tokens
Output cost$0.40/M tokens
Cost per quality point$0.012
Est. monthly (1M tokens/day)$12.00
R1
Input cost$0.70/M tokens
Output cost$2.50/M tokens
Cost per quality point$0.043
Est. monthly (1M tokens/day)$48.00

Llama 3.1 70B Instruct offers 75% better value per quality point. At 1M tokens/day, you'd spend $12.00/month with Llama 3.1 70B Instruct vs $48.00/month with R1 - a $36.00 monthly difference.

Latency & Speed
Llama 3.1 70B InstructFaster
Speed score0/100
R1
Speed score0/100

Both models have comparable response speeds. For most applications, the latency difference is negligible.

When latency matters most: Interactive chatbots, IDE code completion, real-time translation, and user-facing applications where response time directly impacts experience. For batch processing, background summarization, or offline analysis, latency is less critical.

Example Use Cases

Code generation & review

Based on overall model capabilities and architecture for coding tasks like generating functions, debugging, and refactoring

Llama 3.1 70B Instruct

Customer support chatbot

Suitable for user-facing chat with competitive response times. Llama 3.1 70B Instruct also offers lower per-token costs for high-volume support

Llama 3.1 70B Instruct

Long document analysis

Larger context window (131K tokens) can process longer documents, contracts, and research papers in a single pass

Llama 3.1 70B Instruct

Batch data extraction

Lower output pricing ($0.40/M) reduces costs when processing thousands of records daily

Llama 3.1 70B Instruct

Creative writing & content

Higher overall composite score (74/100) correlates with better nuance, coherence, and style in long-form content

R1
Which Should You Choose?
Our recommendation:
R1

R1 has a moderate advantage with a 8.5-point lead in composite score. It wins on more signal dimensions, but Llama 3.1 70B Instruct has specific strengths that could make it the better choice for certain workflows.

by Meta

  • Choose for Quality - Marginally better benchmark scores; both are excellent
  • Choose for Cost - 75% lower pricing; better value at scale
  • Choose for Reliability - Higher uptime and faster response speeds
  • Choose for Prototyping - Stronger community support and better developer experience
  • Choose for Production - Wider enterprise adoption and proven at scale
R1
Recommended

by DeepSeek

Consider for specialized use cases.

Capability Comparison
CapabilityLlama 3.1 70B InstructR1
Vision (Image Input)
Function Calling
Streaming
JSON Mode
Reasoningdiffers
Web Search
Image Output
Monthly Cost Calculator
1,000tokens (600 in / 400 out)
100requests/day (3,000/month)

Llama 3.1 70B Instruct

Meta

Best Value
$1.20
estimated monthly cost

R1

DeepSeek

$4.26
estimated monthly cost

Llama 3.1 70B Instruct saves you $3.06/month

That's 72% cheaper than R1 at 1,000 tokens/request and 100 requests/day.

Assumes 60% input / 40% output token ratio per request. Actual costs may vary based on your usage pattern.

Parameters & Context
ParameterLlama 3.1 70B InstructR1
Context Window131K64K
Max Output Tokens16,38416,000
Open SourceYesYes
CreatedJul 23, 2024Jan 20, 2025
Last updated: 8m ago

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