Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

Claude 3.5 Sonnet vs Sao10K: Llama 3 8B Lunaris benchmark

Name: Claude 3.5 Sonnet vs Sao10K: Llama 3 8B Lunaris AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude 3.5 Sonnet (Anthropic) and Sao10K: Llama 3 8B Lunaris (sao10k) using the latest snapshots we have as of Aug 17, 2026. Sao10K: Llama 3 8B Lunaris wins on output price at $0.05/1M. Claude 3.5 Sonnet 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 3.5 Sonnet

2024 workhorse. Still a high-intent compare against GPT-4o.

Elo 1,280$15/1M out

sao10k

Sao10K: Llama 3 8B Lunaris

Auto-discovered from OpenRouter (sao10k/l3-lunaris-8b). Preview until a second source matches.

$0.049999999999999996/1M out

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Instant pairwise benchmark recalculation across 400+ LLMs

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 3.5 Sonnet (7)vsSao10K: Llama 3 8B Lunaris (2)

Claude 3.5 Sonnet: 7W (78%)Overall: Claude 3.5 SonnetSao10K: Llama 3 8B Lunaris: 2W (22%)

← Claude 3.5 SonnetSao10K: Llama 3 8B Lunaris →

Claude 3.5 Sonnet (4/5)

Intelligence & Reasoning

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

Preference Elo

1,280vs—

Coding Elo

—vs—

SWE-bench

49%vs—

LiveBench

52.1%vs—

GPQA Diamond

65%vs—

Claude 3.5 Sonnet: 4WSao10K: Llama 3 8B Lunaris: 0W

Claude 3.5 Sonnet (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

80 tok/svs—

Time to first token

280 msvs—

Claude 3.5 Sonnet: 2WSao10K: Llama 3 8B Lunaris: 0W

Sao10K: Llama 3 8B Lunaris (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$15/1Mvs$0.05/1M

Input price

$3/1Mvs$0.04/1M

Context window

200kvs8k

Claude 3.5 Sonnet: 1WSao10K: Llama 3 8B Lunaris: 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 3.5 Sonnet 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

Sao10K: Llama 3 8B Lunaris

$14.95/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,280

EloHigh is better

—

LiveBench

High is better

52.1%

LiveBenchHigh is better

—

SWE-bench

High is better

49%

SWE-benchHigh is better

—

GPQA

High is better

65%

GPQAHigh is better

—

TTFT

Low is better

280 ms

TTFTLow is better

—

Speed

High is better

80 tok/s

SpeedHigh is better

—

In $

Low is better

$3/1M

In $Low is better

$0.04/1M+7400%

Out $

Low is better

$15/1M

Out $Low is better

$0.05/1M+29900%

Context

High is better

+2341%200k

ContextHigh is better

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 99% with Sao10K: Llama 3 8B Lunaris

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Claude 3.5 Sonnet$330.00 / mo

In: $105.00Out: $225.00

Sao10K: Llama 3 8B Lunaris$2.15 / mo

In: $1.40Out: $0.75

Estimated Cost Delta

Sao10K: Llama 3 8B Lunaris is estimated to save $327.85/month ($3,934/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Sao10K: Llama 3 8B Lunaris:Pick Sao10K: Llama 3 8B Lunaris when you are optimizing output cost.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatSao10K: Llama 3 8B Lunaris

Lower output list price ($0.05/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGClaude 3.5 Sonnet

Larger window (200k).

Screenshots / visionClaude 3.5 Sonnet

Claude 3.5 Sonnet is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Sao10K: Llama 3 8B Lunaris	Lower output list price ($0.05/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Claude 3.5 Sonnet	Larger window (200k).
Screenshots / vision	Claude 3.5 Sonnet	Claude 3.5 Sonnet is the side marked multimodal in the catalog.

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LIVE

Anthropic

Claude 3.5 Sonnet

Elo 1,280

LiveBench52.1%

SWE-bench49%

Speed80 tok/s

Output cost$15/1M

sao10k

Sao10K: Llama 3 8B Lunaris

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$0.05/1M

Frequently Asked Questions

Which is better overall, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?: We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?: SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?: Sao10K: Llama 3 8B Lunaris output tokens are $0.05/1M versus $15/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?: We do not have TTFT for both models.
Which has the larger context window?: Claude 3.5 Sonnet accepts 200k tokens versus 8k.
Can I self-host either model?: Neither model is marked open-weights here. You are comparing hosted APIs.
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 3.5 Sonnet and Sao10K: Llama 3 8B Lunaris Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Claude 3.5 Sonnet matchups →|All Sao10K: Llama 3 8B Lunaris matchups →

Claude 3.5 Sonnet vs Claude 3.7 Sonnet (next sonnet)Claude Opus 4.5 vs Claude 3.5 Sonnet Claude Opus 4.6 vs Claude 3.5 Sonnet Claude Sonnet 4.5 vs Claude 3.5 Sonnet Claude Opus 5 vs Claude 3.5 Sonnet Claude Fable 5 vs Claude 3.5 Sonnet

Claude 3.5 Sonnet vs Sao10K: Llama 3 8B Lunaris benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude 3.5 Sonnet (7)vsSao10K: Llama 3 8B Lunaris (2)

Claude 3.5 Sonnet: 7W (78%)Overall: Claude 3.5 SonnetSao10K: Llama 3 8B Lunaris: 2W (22%)

← Claude 3.5 SonnetSao10K: Llama 3 8B Lunaris →

Claude 3.5 Sonnet (4/5)

Intelligence & Reasoning

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

Preference Elo

1,280vs—

Coding Elo

—vs—

SWE-bench

49%vs—

LiveBench

52.1%vs—

GPQA Diamond

65%vs—

Claude 3.5 Sonnet: 4WSao10K: Llama 3 8B Lunaris: 0W

Claude 3.5 Sonnet (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

80 tok/svs—

Time to first token

280 msvs—

Claude 3.5 Sonnet: 2WSao10K: Llama 3 8B Lunaris: 0W

Sao10K: Llama 3 8B Lunaris (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$15/1Mvs$0.05/1M

Input price

$3/1Mvs$0.04/1M

Context window

200kvs8k

Claude 3.5 Sonnet: 1WSao10K: Llama 3 8B Lunaris: 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatSao10K: Llama 3 8B Lunaris

Lower output list price ($0.05/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGClaude 3.5 Sonnet

Larger window (200k).

Screenshots / visionClaude 3.5 Sonnet

Claude 3.5 Sonnet is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	insufficient data	Need SWE-bench on both sides.
High-volume chat	Sao10K: Llama 3 8B Lunaris	Lower output list price ($0.05/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Claude 3.5 Sonnet	Larger window (200k).
Screenshots / vision	Claude 3.5 Sonnet	Claude 3.5 Sonnet is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?

We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.

Which is better at coding, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?

SWE-bench is missing for at least one model, so we do not rank coding from a single number.

Which is cheaper to run in production?

Sao10K: Llama 3 8B Lunaris output tokens are $0.05/1M versus $15/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude 3.5 Sonnet or Sao10K: Llama 3 8B Lunaris?

We do not have TTFT for both models.

Which has the larger context window?

Claude 3.5 Sonnet accepts 200k tokens versus 8k.

Can I self-host either model?

Neither model is marked open-weights here. You are comparing hosted APIs.

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 3.5 Sonnet and Sao10K: Llama 3 8B Lunaris Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.