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

Claude Opus 4.8 vs OpenAI: GPT-3.5 Turbo (older v0613) benchmark

Name: Claude Opus 4.8 vs OpenAI: GPT-3.5 Turbo (older v0613) AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude Opus 4.8 (Anthropic) and OpenAI: GPT-3.5 Turbo (older v0613) (OpenAI) using the latest snapshots we have as of Aug 17, 2026. OpenAI: GPT-3.5 Turbo (older v0613) wins on output price at $2/1M. Claude Opus 4.8 has the larger context window (1M 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 4.8

Prior Opus generation still billed at $5/$25. Kept as a compare baseline against Opus 5.

Elo 1,598$25/1M out

OpenAI

OpenAI: GPT-3.5 Turbo (older v0613)

Auto-discovered from OpenRouter (openai/gpt-3.5-turbo-0613). Preview until a second source matches.

$2/1M out

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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 4.8 (8)vsOpenAI: GPT-3.5 Turbo (older v0613) (2)

Claude Opus 4.8: 8W (80%)Overall: Claude Opus 4.8OpenAI: GPT-3.5 Turbo (older v0613): 2W (20%)

← Claude Opus 4.8OpenAI: GPT-3.5 Turbo (older v0613) →

Claude Opus 4.8 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,598vs—

Coding Elo

1,604vs—

SWE-bench

78.4%vs—

LiveBench

73.6%vs—

GPQA Diamond

86.2%vs—

Claude Opus 4.8: 5WOpenAI: GPT-3.5 Turbo (older v0613): 0W

Claude Opus 4.8 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs—

Time to first token

330 msvs—

Claude Opus 4.8: 2WOpenAI: GPT-3.5 Turbo (older v0613): 0W

OpenAI: GPT-3.5 Turbo (older v0613) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$25/1Mvs$2/1M

Input price

$5/1Mvs$1/1M

Context window

1Mvs4k

Claude Opus 4.8: 1WOpenAI: GPT-3.5 Turbo (older v0613): 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 4.8 5/6

Preference Elo

No shared data

Throughput Speed

No shared data

Price Efficiency

OpenAI: GPT-3.5 Turbo (older v0613)

$23.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,598

EloHigh is better

—

Code Elo

High is better

1,604

Code EloHigh is better

—

LiveBench

High is better

73.6%

LiveBenchHigh is better

—

SWE-bench

High is better

78.4%

SWE-benchHigh is better

—

GPQA

High is better

86.2%

GPQAHigh is better

—

TTFT

Low is better

330 ms

TTFTLow is better

—

Speed

High is better

66 tok/s

SpeedHigh is better

—

In $

Low is better

$5/1M

In $Low is better

$1/1M+400%

Out $

Low is better

$25/1M

Out $Low is better

$2/1M+1150%

Context

High is better

+24320%1M

ContextHigh is better

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 88% with OpenAI: GPT-3.5 Turbo (older v0613)

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 4.8$550.00 / mo

In: $175.00Out: $375.00

OpenAI: GPT-3.5 Turbo (older v0613)$65.00 / mo

In: $35.00Out: $30.00

Estimated Cost Delta

OpenAI: GPT-3.5 Turbo (older v0613) is estimated to save $485.00/month ($5,820/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: GPT-3.5 Turbo (older v0613):Pick OpenAI: GPT-3.5 Turbo (older v0613) when you are optimizing output cost.
2
Claude Opus 4.8:Pick Claude Opus 4.8 for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-3.5 Turbo (older v0613)

Lower output list price ($2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGClaude Opus 4.8

Larger window (1M).

Screenshots / visionClaude Opus 4.8

Claude Opus 4.8 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	OpenAI: GPT-3.5 Turbo (older v0613)	Lower output list price ($2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Claude Opus 4.8	Larger window (1M).
Screenshots / vision	Claude Opus 4.8	Claude Opus 4.8 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchJun 20, 2026

Keep Opus 4.8 on the board: a baseline, not a flagship

Opus 4.8 still bills $5/$25. We keep it so Opus 5 vs 4.8 is a real generation delta, not marketing.

Community Sentiment

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LIVE

Anthropic

Claude Opus 4.8

Elo 1,598

LiveBench73.6%

SWE-bench78.4%

Speed66 tok/s

Output cost$25/1M

OpenAI

OpenAI: GPT-3.5 Turbo (older v0613)

Elo n/a

LiveBench—

SWE-bench—

Speed—

Output cost$2/1M

Frequently Asked Questions

Which is better overall, Claude Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?: 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 Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?: 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?: OpenAI: GPT-3.5 Turbo (older v0613) output tokens are $2/1M versus $25/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?: We do not have TTFT for both models.
Which has the larger context window?: Claude Opus 4.8 accepts 1M tokens versus 4k.
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 Opus 4.8 and OpenAI: GPT-3.5 Turbo (older v0613) Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Claude Opus 4.8 matchups →|All OpenAI: GPT-3.5 Turbo (older v0613) matchups →

Claude Opus 4.8 vs Claude Opus 4.6 (previous opus)Claude Opus 4.8 vs Claude Opus 5 (next opus)GPT-5.6 Sol vs Claude Opus 4.8 Claude Fable 5 vs Claude Opus 4.8 Claude Opus 4.5 vs Claude Opus 4.8 GPT-5 vs Claude Opus 4.8

Claude Opus 4.8 vs OpenAI: GPT-3.5 Turbo (older v0613) benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude Opus 4.8 (8)vsOpenAI: GPT-3.5 Turbo (older v0613) (2)

Claude Opus 4.8: 8W (80%)Overall: Claude Opus 4.8OpenAI: GPT-3.5 Turbo (older v0613): 2W (20%)

← Claude Opus 4.8OpenAI: GPT-3.5 Turbo (older v0613) →

Claude Opus 4.8 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,598vs—

Coding Elo

1,604vs—

SWE-bench

78.4%vs—

LiveBench

73.6%vs—

GPQA Diamond

86.2%vs—

Claude Opus 4.8: 5WOpenAI: GPT-3.5 Turbo (older v0613): 0W

Claude Opus 4.8 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs—

Time to first token

330 msvs—

Claude Opus 4.8: 2WOpenAI: GPT-3.5 Turbo (older v0613): 0W

OpenAI: GPT-3.5 Turbo (older v0613) (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$25/1Mvs$2/1M

Input price

$5/1Mvs$1/1M

Context window

1Mvs4k

Claude Opus 4.8: 1WOpenAI: GPT-3.5 Turbo (older v0613): 2W

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatOpenAI: GPT-3.5 Turbo (older v0613)

Lower output list price ($2/1M).

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

Long-document RAGClaude Opus 4.8

Larger window (1M).

Screenshots / visionClaude Opus 4.8

Claude Opus 4.8 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	OpenAI: GPT-3.5 Turbo (older v0613)	Lower output list price ($2/1M).
Voice / low-latency UI	insufficient data	Need TTFT on both sides.
Long-document RAG	Claude Opus 4.8	Larger window (1M).
Screenshots / vision	Claude Opus 4.8	Claude Opus 4.8 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Claude Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?

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 Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?

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?

OpenAI: GPT-3.5 Turbo (older v0613) output tokens are $2/1M versus $25/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude Opus 4.8 or OpenAI: GPT-3.5 Turbo (older v0613)?

We do not have TTFT for both models.

Which has the larger context window?

Claude Opus 4.8 accepts 1M tokens versus 4k.

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 Opus 4.8 and OpenAI: GPT-3.5 Turbo (older v0613) 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

No shared data

No shared data

OpenAI: GPT-3.5 Turbo (older v0613)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Keep Opus 4.8 on the board: a baseline, not a flagship

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

Claude Opus 4.8

OpenAI: GPT-3.5 Turbo (older v0613)

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

No shared data

No shared data

OpenAI: GPT-3.5 Turbo (older v0613)

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Keep Opus 4.8 on the board: a baseline, not a flagship

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

Claude Opus 4.8

OpenAI: GPT-3.5 Turbo (older v0613)

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons