Pairwise benchmark snapshot · Aug 16, 2026

Claude Opus 4.5 vs Llama 3.3 70B benchmark

Name: Claude Opus 4.5 vs Llama 3.3 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude Opus 4.5 (Anthropic) and Llama 3.3 70B (Meta) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Claude Opus 4.5 is ahead by 283 points (1,568 vs 1,285; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Claude Opus 4.5 resolves 76.8% versus 45.1% (seed-bootstrap (Aug 1, 2026)). Llama 3.3 70B streams faster (140 tok/s) while Llama 3.3 70B is the cheaper output token ($0.32/1M). Claude Opus 4.5 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 4.5

Anthropic frontier coding and computer-use model. SWE-bench leader on the official mini-SWE-agent harness in the Feb 2026 refresh.

Elo 1,568$25/1M out

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Quick vs:vs Claude Opus 4.6 vs Claude Sonnet 4.5 vs Claude 3.7 Sonnet vs GPT-5

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude Opus 4.5 (6)vsLlama 3.3 70B (4)

Claude Opus 4.5: 6W (60%)Overall: Claude Opus 4.5Llama 3.3 70B: 4W (40%)

← Claude Opus 4.5Llama 3.3 70B →

Claude Opus 4.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,568vs1,285

Coding Elo

1,574vs—

SWE-bench

76.8%vs45.1%

LiveBench

71.2%vs49.8%

GPQA Diamond

84.1%vs65.7%

Claude Opus 4.5: 5WLlama 3.3 70B: 0W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs140 tok/s

Time to first token

380 msvs160 ms

Claude Opus 4.5: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$25/1Mvs$0.32/1M

Input price

$5/1Mvs$0.1/1M

Context window

200kvs131k

Claude Opus 4.5: 1WLlama 3.3 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 4.5 4/6

Preference Elo

Claude Opus 4.5

283 pts advantage

Throughput Speed

Llama 3.3 70B

78 tok/s faster

Price Efficiency

Llama 3.3 70B

$24.68/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+22%1,568

EloHigh is better

1,285

Code Elo

High is better

1,574

Code EloHigh is better

—

LiveBench

High is better

+43%71.2%

LiveBenchHigh is better

49.8%

SWE-bench

High is better

+70%76.8%

SWE-benchHigh is better

45.1%

GPQA

High is better

+28%84.1%

GPQAHigh is better

65.7%

TTFT

Low is better

380 ms

TTFTLow is better

160 ms+138%

Speed

High is better

62 tok/s

SpeedHigh is better

140 tok/s+126%

In $

Low is better

$5/1M

In $Low is better

$0.1/1M+4900%

Out $

Low is better

$25/1M

Out $Low is better

$0.32/1M+7713%

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 98% with Llama 3.3 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 4.5$550.00 / mo

In: $175.00Out: $375.00

Llama 3.3 70B$8.30 / mo

In: $3.50Out: $4.80

Estimated Cost Delta

Llama 3.3 70B is estimated to save $541.70/month ($6,500/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Claude Opus 4.5:Pick Claude Opus 4.5 when repo-level coding accuracy is the constraint.
2
Llama 3.3 70B:Pick Llama 3.3 70B when you are optimizing output cost.
3
Llama 3.3 70B:Pick Llama 3.3 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsClaude Opus 4.5

Higher SWE-bench (76.8%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGClaude Opus 4.5

Larger window (200k).

Screenshots / visionClaude Opus 4.5

Claude Opus 4.5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude Opus 4.5	Higher SWE-bench (76.8%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Claude Opus 4.5	Larger window (200k).
Screenshots / vision	Claude Opus 4.5	Claude Opus 4.5 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchFeb 20, 2026

Claude Opus 4.5 and the Feb 2026 SWE-bench row we still cite

The Feb refresh made 4.5 a coding reference. Newer Opus SKUs exist; this row stays as the dated harness point.

launchDec 6, 2024

Llama 3.3 70B stays as the previous Meta 70B workhorse

Dec 2024 instruct 70B. Still a self-host baseline. Llama 4 is the 2026 buy.

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LIVE

Anthropic

Claude Opus 4.5

Elo 1,568

LiveBench71.2%

SWE-bench76.8%

Speed62 tok/s

Output cost$25/1M

Llama 3.3 70B

Elo 1,285

LiveBench49.8%

SWE-bench45.1%

Speed140 tok/s

Output cost$0.32/1M

Frequently Asked Questions

Which is better overall, Claude Opus 4.5 or Llama 3.3 70B?: Claude Opus 4.5 has the higher preference Elo in our latest snapshot (1,568). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Claude Opus 4.5 or Llama 3.3 70B?: Claude Opus 4.5 leads SWE-bench at 76.8% vs 45.1%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.3 70B output tokens are $0.32/1M versus $25/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude Opus 4.5 or Llama 3.3 70B?: Llama 3.3 70B has the lower time-to-first-token (160 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Claude Opus 4.5 accepts 200k tokens versus 131k.
Can I self-host either model?: Llama 3.3 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 4.5 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Claude Opus 4.5 matchups →|All Llama 3.3 70B matchups →

Claude Opus 4.5 vs Claude Opus 4 (previous opus)Claude Opus 4.5 vs Claude Opus 4.6 (next opus)Llama 3.3 70B vs Llama 3.1 70B (previous llama)Llama 3.3 70B vs Llama 4 Maverick (next llama)Claude Sonnet 5 vs Claude Opus 4.5 Claude Opus 4.8 vs Claude Opus 4.5

Claude Opus 4.5 vs Llama 3.3 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude Opus 4.5 (6)vsLlama 3.3 70B (4)

Claude Opus 4.5: 6W (60%)Overall: Claude Opus 4.5Llama 3.3 70B: 4W (40%)

← Claude Opus 4.5Llama 3.3 70B →

Claude Opus 4.5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,568vs1,285

Coding Elo

1,574vs—

SWE-bench

76.8%vs45.1%

LiveBench

71.2%vs49.8%

GPQA Diamond

84.1%vs65.7%

Claude Opus 4.5: 5WLlama 3.3 70B: 0W

Llama 3.3 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs140 tok/s

Time to first token

380 msvs160 ms

Claude Opus 4.5: 0WLlama 3.3 70B: 2W

Llama 3.3 70B (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$25/1Mvs$0.32/1M

Input price

$5/1Mvs$0.1/1M

Context window

200kvs131k

Claude Opus 4.5: 1WLlama 3.3 70B: 2W

Recommended Workload Routing

Repo / coding agentsClaude Opus 4.5

Higher SWE-bench (76.8%).

High-volume chatLlama 3.3 70B

Lower output list price ($0.32/1M).

Voice / low-latency UILlama 3.3 70B

Lower TTFT (160 ms).

Long-document RAGClaude Opus 4.5

Larger window (200k).

Screenshots / visionClaude Opus 4.5

Claude Opus 4.5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude Opus 4.5	Higher SWE-bench (76.8%).
High-volume chat	Llama 3.3 70B	Lower output list price ($0.32/1M).
Voice / low-latency UI	Llama 3.3 70B	Lower TTFT (160 ms).
Long-document RAG	Claude Opus 4.5	Larger window (200k).
Screenshots / vision	Claude Opus 4.5	Claude Opus 4.5 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Claude Opus 4.5 or Llama 3.3 70B?

Claude Opus 4.5 has the higher preference Elo in our latest snapshot (1,568). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Claude Opus 4.5 or Llama 3.3 70B?

Claude Opus 4.5 leads SWE-bench at 76.8% vs 45.1%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.3 70B output tokens are $0.32/1M versus $25/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude Opus 4.5 or Llama 3.3 70B?

Llama 3.3 70B has the lower time-to-first-token (160 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Claude Opus 4.5 accepts 200k tokens versus 131k.

Can I self-host either model?

Llama 3.3 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 4.5 and Llama 3.3 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Claude Opus 4.5

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Claude Opus 4.5 and the Feb 2026 SWE-bench row we still cite

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Claude Opus 4.5

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

Claude Opus 4.5

Llama 3.3 70B

Llama 3.3 70B

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Claude Opus 4.5 and the Feb 2026 SWE-bench row we still cite

Llama 3.3 70B stays as the previous Meta 70B workhorse

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Claude Opus 4.5

Llama 3.3 70B

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons