Pairwise benchmark snapshot · Aug 16, 2026

Claude Opus 4 vs Llama 3.1 70B benchmark

Name: Claude Opus 4 vs Llama 3.1 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude Opus 4 (Anthropic) and Llama 3.1 70B (Meta) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Claude Opus 4 is ahead by 250 points (1,490 vs 1,240; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Claude Opus 4 resolves 72.5% versus 40.2% (seed-bootstrap (Aug 1, 2026)). Llama 3.1 70B streams faster (125 tok/s) while Llama 3.1 70B is the cheaper output token ($0.4/1M). Claude Opus 4 has the larger context window (200k tokens). Every cell below is a dated snapshot from a named public source — we do not invent missing scores, and we do not run SWE-bench ourselves.

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Anthropic

Claude Opus 4

First Claude 4 Opus generation. Baseline for opus 4 vs opus 5.

Elo 1,490$75/1M out

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Model A

Model B

Quick vs:vs Claude Opus 4.5 vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude Opus 4 (6)vsLlama 3.1 70B (4)

Claude Opus 4: 6W (60%)Overall: Claude Opus 4Llama 3.1 70B: 4W (40%)

← Claude Opus 4Llama 3.1 70B →

Claude Opus 4 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,490vs1,240

Coding Elo

1,502vs—

SWE-bench

72.5%vs40.2%

LiveBench

66.8%vs46.8%

GPQA Diamond

80.4%vs48%

Claude Opus 4: 5WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

58 tok/svs125 tok/s

Time to first token

390 msvs170 ms

Claude Opus 4: 0WLlama 3.1 70B: 2W

Llama 3.1 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$75/1Mvs$0.4/1M

Input price

$15/1Mvs$0.4/1M

Context window

200kvs131k

Claude Opus 4: 1WLlama 3.1 70B: 2W

Capability Matchup Radar

Multi-Dimensional Capability Radar

Each spoke is a skill. Farther from the center is better (0–100th percentile). Tap any dot to inspect details.

Tap any node to inspect

Percentile 0–100

Dimensional AdvantageClaude Opus 4 4/6

Preference Elo

Claude Opus 4

250 pts advantage

Throughput Speed

Llama 3.1 70B

67 tok/s faster

Price Efficiency

Llama 3.1 70B

$74.60/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+20%1,490

EloHigh is better

1,240

Code Elo

High is better

1,502

Code EloHigh is better

—

LiveBench

High is better

+43%66.8%

LiveBenchHigh is better

46.8%

SWE-bench

High is better

+80%72.5%

SWE-benchHigh is better

40.2%

GPQA

High is better

+68%80.4%

GPQAHigh is better

48%

TTFT

Low is better

390 ms

TTFTLow is better

170 ms+129%

Speed

High is better

58 tok/s

SpeedHigh is better

125 tok/s+116%

In $

Low is better

$15/1M

In $Low is better

$0.4/1M+3650%

Out $

Low is better

$75/1M

Out $Low is better

$0.4/1M+18650%

Context

High is better

+53%200k

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

Simulate monthly production API costs based on published $/1M tokens.

Save up to 99% with Llama 3.1 70B

Monthly Volume50M tokens/mo

Quick Presets:

Token Ratio (In vs Out)70% In / 30% Out

RAG / Search (10% out)Coding / Chat (50% out)

Claude Opus 4$1,650.00 / mo

In: $525.00Out: $1125.00

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

Estimated Cost Delta

Llama 3.1 70B is estimated to save $1,630.00/month ($19,560/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Claude Opus 4:Pick Claude Opus 4 when repo-level coding accuracy is the constraint.
2
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.
3
Llama 3.1 70B:Pick Llama 3.1 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsClaude Opus 4

Higher SWE-bench (72.5%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

Long-document RAGClaude Opus 4

Larger window (200k).

Screenshots / visionClaude Opus 4

Claude Opus 4 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude Opus 4	Higher SWE-bench (72.5%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	Claude Opus 4	Larger window (200k).
Screenshots / vision	Claude Opus 4	Claude Opus 4 is the side marked multimodal in the catalog.

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LIVE

Anthropic

Claude Opus 4

Elo 1,490

LiveBench66.8%

SWE-bench72.5%

Speed58 tok/s

Output cost$75/1M

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, Claude Opus 4 or Llama 3.1 70B?: Claude Opus 4 has the higher preference Elo in our latest snapshot (1,490). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Claude Opus 4 or Llama 3.1 70B?: Claude Opus 4 leads SWE-bench at 72.5% vs 40.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.1 70B output tokens are $0.4/1M versus $75/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude Opus 4 or Llama 3.1 70B?: Llama 3.1 70B has the lower time-to-first-token (170 ms vs 390 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Claude Opus 4 accepts 200k tokens versus 131k.
Can I self-host either model?: Llama 3.1 70B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
Are these scores from CompareLLM’s own evals?: No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.
What does preference Elo mean on this page?: Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Claude Opus 4 and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Claude Opus 4 matchups →|All Llama 3.1 70B matchups →

Claude Opus 4 vs Claude 3 Opus (previous opus)Claude Opus 4 vs Claude Opus 4.5 (next opus)Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Llama 4 Maverick vs Claude Opus 4 Claude Sonnet 4.5 vs Claude Opus 4

Claude Opus 4 vs Llama 3.1 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude Opus 4 (6)vsLlama 3.1 70B (4)

Claude Opus 4: 6W (60%)Overall: Claude Opus 4Llama 3.1 70B: 4W (40%)

← Claude Opus 4Llama 3.1 70B →

Claude Opus 4 (5/5)

Intelligence & Reasoning

Preference Elo, Coding proficiency, SWE-bench & LiveBench accuracy

Preference Elo

1,490vs1,240

Coding Elo

1,502vs—

SWE-bench

72.5%vs40.2%

LiveBench

66.8%vs46.8%

GPQA Diamond

80.4%vs48%

Claude Opus 4: 5WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

58 tok/svs125 tok/s

Time to first token

390 msvs170 ms

Claude Opus 4: 0WLlama 3.1 70B: 2W

Llama 3.1 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$75/1Mvs$0.4/1M

Input price

$15/1Mvs$0.4/1M

Context window

200kvs131k

Claude Opus 4: 1WLlama 3.1 70B: 2W

Recommended Workload Routing

Repo / coding agentsClaude Opus 4

Higher SWE-bench (72.5%).

High-volume chatLlama 3.1 70B

Lower output list price ($0.4/1M).

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 ms).

Long-document RAGClaude Opus 4

Larger window (200k).

Screenshots / visionClaude Opus 4

Claude Opus 4 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude Opus 4	Higher SWE-bench (72.5%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	Claude Opus 4	Larger window (200k).
Screenshots / vision	Claude Opus 4	Claude Opus 4 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Claude Opus 4 or Llama 3.1 70B?

Claude Opus 4 has the higher preference Elo in our latest snapshot (1,490). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Claude Opus 4 or Llama 3.1 70B?

Claude Opus 4 leads SWE-bench at 72.5% vs 40.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.1 70B output tokens are $0.4/1M versus $75/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude Opus 4 or Llama 3.1 70B?

Llama 3.1 70B has the lower time-to-first-token (170 ms vs 390 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Claude Opus 4 accepts 200k tokens versus 131k.

Can I self-host either model?

Llama 3.1 70B is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

Are these scores from CompareLLM’s own evals?

No. V1 aggregates public leaderboards (and optional first-party latency pings). Each cell names its source and date. Read /methodology.

What does preference Elo mean on this page?

Preference Elo is a crowd vote from LMArena / Arena. People see two hidden answers and pick the one they like more. The model that wins more often gets a higher Elo. That means people preferred it — not that it passed a school test. It is not SWE-bench, not accuracy, and not a number we invent. Claude Opus 4 and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.