Pairwise benchmark snapshot · Aug 17, 2026

Claude 3.5 Haiku vs GPT-4 Turbo benchmark

Name: Claude 3.5 Haiku vs GPT-4 Turbo AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude 3.5 Haiku (Anthropic) and GPT-4 Turbo (OpenAI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, GPT-4 Turbo is ahead by 31 points (1,255 vs 1,224; lmarena (Aug 17, 2026)). On SWE-bench coding, Claude 3.5 Haiku resolves 36.2% versus 33.2% (swebench (Aug 17, 2026)). Claude 3.5 Haiku streams faster (140 tok/s) while Claude 3.5 Haiku is the cheaper output token ($4/1M). Claude 3.5 Haiku 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 Haiku

Previous cheap Claude. Haiku 4.5 is the current $1/$5 SKU.

Elo 1,224$4/1M out

OpenAI

GPT-4 Turbo

GPT-4 Turbo 128k. Historical flagship for gpt-4 turbo vs gpt-4o / gpt-5.

Elo 1,255$30/1M 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 3.5 Haiku (7)vsGPT-4 Turbo (2)

Claude 3.5 Haiku: 7W (78%)Overall: Claude 3.5 HaikuGPT-4 Turbo: 2W (22%)

← Claude 3.5 HaikuGPT-4 Turbo →

Tied (2/5)

Intelligence & Reasoning

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

Preference Elo

1,224vs1,255

Coding Elo

—vs—

SWE-bench

36.2%vs33.2%

LiveBench

44.8%vs47%

GPQA Diamond

54.1%vs48%

Claude 3.5 Haiku: 2WGPT-4 Turbo: 2W

Claude 3.5 Haiku (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs55 tok/s

Time to first token

110 msvs350 ms

Claude 3.5 Haiku: 2WGPT-4 Turbo: 0W

Claude 3.5 Haiku (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$30/1M

Input price

$0.8/1Mvs$10/1M

Context window

200kvs128k

Claude 3.5 Haiku: 3WGPT-4 Turbo: 0W

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 Haiku 4/6

Preference Elo

GPT-4 Turbo

31 pts advantage

Throughput Speed

Claude 3.5 Haiku

85 tok/s faster

Price Efficiency

Claude 3.5 Haiku

$26.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,224

EloHigh is better

1,255+3%

LiveBench

High is better

44.8%

LiveBenchHigh is better

47%+5%

SWE-bench

High is better

+9%36.2%

SWE-benchHigh is better

33.2%

GPQA

High is better

+13%54.1%

GPQAHigh is better

48%

TTFT

Low is better

+218%110 ms

TTFTLow is better

350 ms

Speed

High is better

+155%140 tok/s

SpeedHigh is better

55 tok/s

In $

Low is better

+1150%$0.8/1M

In $Low is better

$10/1M

Out $

Low is better

+650%$4/1M

Out $Low is better

$30/1M

Context

High is better

+56%200k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 89% with Claude 3.5 Haiku

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 Haiku$88.00 / mo

In: $28.00Out: $60.00

GPT-4 Turbo$800.00 / mo

In: $350.00Out: $450.00

Estimated Cost Delta

Claude 3.5 Haiku is estimated to save $712.00/month ($8,544/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Claude 3.5 Haiku:Pick Claude 3.5 Haiku when repo-level coding accuracy is the constraint.
2
Claude 3.5 Haiku:Pick Claude 3.5 Haiku when time-to-first-token matters more than peak Elo.
3
GPT-4 Turbo:Pick GPT-4 Turbo as the default general assistant.

Recommended Workload Routing

Repo / coding agentsClaude 3.5 Haiku

Higher SWE-bench (36.2%).

High-volume chatClaude 3.5 Haiku

Lower output list price ($4/1M).

Voice / low-latency UIClaude 3.5 Haiku

Lower TTFT (110 ms).

Long-document RAGClaude 3.5 Haiku

Larger window (200k).

Screenshots / visionGPT-4 Turbo

Both accept images. Defaulting to the higher-Elo side (GPT-4 Turbo).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude 3.5 Haiku	Higher SWE-bench (36.2%).
High-volume chat	Claude 3.5 Haiku	Lower output list price ($4/1M).
Voice / low-latency UI	Claude 3.5 Haiku	Lower TTFT (110 ms).
Long-document RAG	Claude 3.5 Haiku	Larger window (200k).
Screenshots / vision	GPT-4 Turbo	Both accept images. Defaulting to the higher-Elo side (GPT-4 Turbo).

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CompareLLM Matrix

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LIVE

Anthropic

Claude 3.5 Haiku

Elo 1,224

LiveBench44.8%

SWE-bench36.2%

Speed140 tok/s

Output cost$4/1M

OpenAI

GPT-4 Turbo

Elo 1,255

LiveBench47%

SWE-bench33.2%

Speed55 tok/s

Output cost$30/1M

Frequently Asked Questions

Which is better overall, Claude 3.5 Haiku or GPT-4 Turbo?: GPT-4 Turbo has the higher preference Elo in our latest snapshot (1,255). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Claude 3.5 Haiku or GPT-4 Turbo?: Claude 3.5 Haiku leads SWE-bench at 36.2% vs 33.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Claude 3.5 Haiku output tokens are $4/1M versus $30/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude 3.5 Haiku or GPT-4 Turbo?: Claude 3.5 Haiku has the lower time-to-first-token (110 ms vs 350 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Claude 3.5 Haiku accepts 200k tokens versus 128k.
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 Haiku and GPT-4 Turbo Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Claude 3.5 Haiku matchups →|All GPT-4 Turbo matchups →

Claude 3.5 Haiku vs Claude Haiku 4.5 (next haiku)GPT-4 Turbo vs GPT-4o (next gpt-flagship)Claude Opus 4.5 vs GPT-4 Turbo Claude Opus 4.6 vs GPT-4 Turbo Claude Sonnet 4.5 vs GPT-4 Turbo GPT-5 vs GPT-4 Turbo

Claude 3.5 Haiku vs GPT-4 Turbo benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude 3.5 Haiku (7)vsGPT-4 Turbo (2)

Claude 3.5 Haiku: 7W (78%)Overall: Claude 3.5 HaikuGPT-4 Turbo: 2W (22%)

← Claude 3.5 HaikuGPT-4 Turbo →

Tied (2/5)

Intelligence & Reasoning

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

Preference Elo

1,224vs1,255

Coding Elo

—vs—

SWE-bench

36.2%vs33.2%

LiveBench

44.8%vs47%

GPQA Diamond

54.1%vs48%

Claude 3.5 Haiku: 2WGPT-4 Turbo: 2W

Claude 3.5 Haiku (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs55 tok/s

Time to first token

110 msvs350 ms

Claude 3.5 Haiku: 2WGPT-4 Turbo: 0W

Claude 3.5 Haiku (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$30/1M

Input price

$0.8/1Mvs$10/1M

Context window

200kvs128k

Claude 3.5 Haiku: 3WGPT-4 Turbo: 0W

Recommended Workload Routing

Repo / coding agentsClaude 3.5 Haiku

Higher SWE-bench (36.2%).

High-volume chatClaude 3.5 Haiku

Lower output list price ($4/1M).

Voice / low-latency UIClaude 3.5 Haiku

Lower TTFT (110 ms).

Long-document RAGClaude 3.5 Haiku

Larger window (200k).

Screenshots / visionGPT-4 Turbo

Both accept images. Defaulting to the higher-Elo side (GPT-4 Turbo).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Claude 3.5 Haiku	Higher SWE-bench (36.2%).
High-volume chat	Claude 3.5 Haiku	Lower output list price ($4/1M).
Voice / low-latency UI	Claude 3.5 Haiku	Lower TTFT (110 ms).
Long-document RAG	Claude 3.5 Haiku	Larger window (200k).
Screenshots / vision	GPT-4 Turbo	Both accept images. Defaulting to the higher-Elo side (GPT-4 Turbo).

Frequently Asked Questions

Which is better overall, Claude 3.5 Haiku or GPT-4 Turbo?

GPT-4 Turbo has the higher preference Elo in our latest snapshot (1,255). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Claude 3.5 Haiku or GPT-4 Turbo?

Claude 3.5 Haiku leads SWE-bench at 36.2% vs 33.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Claude 3.5 Haiku output tokens are $4/1M versus $30/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude 3.5 Haiku or GPT-4 Turbo?

Claude 3.5 Haiku has the lower time-to-first-token (110 ms vs 350 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Claude 3.5 Haiku accepts 200k tokens versus 128k.

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 Haiku and GPT-4 Turbo Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.