Pairwise benchmark snapshot · Aug 16, 2026

Claude 3.5 Haiku vs OpenAI: o1 benchmark

Name: Claude 3.5 Haiku vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Claude 3.5 Haiku (Anthropic) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 288 points (1,512 vs 1,224; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, OpenAI: o1 resolves 48.9% versus 36.2% (seed-bootstrap (Aug 1, 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

OpenAI: o1

Auto-discovered from OpenRouter (openai/o1). Preview until a second source matches.

Elo 1,512$60/1M 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 (4)vsOpenAI: o1 (5)

Claude 3.5 Haiku: 4W (44%)Overall: OpenAI: o1OpenAI: o1: 5W (56%)

← Claude 3.5 Haiku1 tiesOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,224vs1,512

Coding Elo

—vs1,485

SWE-bench

36.2%vs48.9%

LiveBench

44.8%vs65.4%

GPQA Diamond

54.1%vs78%

Claude 3.5 Haiku: 0WOpenAI: o1: 5W

Claude 3.5 Haiku (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs45 tok/s

Time to first token

110 msvs420 ms

Claude 3.5 Haiku: 2WOpenAI: o1: 0W

Claude 3.5 Haiku (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$60/1M

Input price

$0.8/1Mvs$15/1M

Context window

200kvs200k

Claude 3.5 Haiku: 2WOpenAI: o1: 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 AdvantageOpenAI: o1 3/6

Preference Elo

OpenAI: o1

288 pts advantage

Throughput Speed

Claude 3.5 Haiku

95 tok/s faster

Price Efficiency

Claude 3.5 Haiku

$56.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,224

EloHigh is better

1,512+24%

Code Elo

High is better

—

Code EloHigh is better

1,485

LiveBench

High is better

44.8%

LiveBenchHigh is better

65.4%+46%

SWE-bench

High is better

36.2%

SWE-benchHigh is better

48.9%+35%

GPQA

High is better

54.1%

GPQAHigh is better

78%+44%

TTFT

Low is better

+282%110 ms

TTFTLow is better

420 ms

Speed

High is better

+211%140 tok/s

SpeedHigh is better

45 tok/s

In $

Low is better

+1775%$0.8/1M

In $Low is better

$15/1M

Out $

Low is better

+1400%$4/1M

Out $Low is better

$60/1M

Context

High is better

200k

ContextHigh is better

200k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 94% 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

OpenAI: o1$1,425.00 / mo

In: $525.00Out: $900.00

Estimated Cost Delta

Claude 3.5 Haiku is estimated to save $1,337.00/month ($16,044/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI: o1:Pick OpenAI: o1 when repo-level coding accuracy is the constraint.
2
Claude 3.5 Haiku:Pick Claude 3.5 Haiku when you are optimizing output cost.
3
Claude 3.5 Haiku:Pick Claude 3.5 Haiku when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

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 / visionOpenAI: o1

Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
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	OpenAI: o1	Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

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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

OpenAI: o1

Elo 1,512

LiveBench65.4%

SWE-bench48.9%

Speed45 tok/s

Output cost$60/1M

Frequently Asked Questions

Which is better overall, Claude 3.5 Haiku or OpenAI: o1?: OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, Claude 3.5 Haiku or OpenAI: o1?: OpenAI: o1 leads SWE-bench at 48.9% vs 36.2%. 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 $60/1M. Input prices and retry rates still move the real bill.
Which is faster, Claude 3.5 Haiku or OpenAI: o1?: Claude 3.5 Haiku has the lower time-to-first-token (110 ms vs 420 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 200k.
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 OpenAI: o1 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 OpenAI: o1 matchups →

Claude 3.5 Haiku vs Claude Haiku 4.5 (next haiku)OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)Claude Opus 4.5 vs OpenAI: o1 Claude Opus 4.6 vs OpenAI: o1 Claude Sonnet 4.5 vs OpenAI: o1

Claude 3.5 Haiku vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Claude 3.5 Haiku (4)vsOpenAI: o1 (5)

Claude 3.5 Haiku: 4W (44%)Overall: OpenAI: o1OpenAI: o1: 5W (56%)

← Claude 3.5 Haiku1 tiesOpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,224vs1,512

Coding Elo

—vs1,485

SWE-bench

36.2%vs48.9%

LiveBench

44.8%vs65.4%

GPQA Diamond

54.1%vs78%

Claude 3.5 Haiku: 0WOpenAI: o1: 5W

Claude 3.5 Haiku (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

140 tok/svs45 tok/s

Time to first token

110 msvs420 ms

Claude 3.5 Haiku: 2WOpenAI: o1: 0W

Claude 3.5 Haiku (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$4/1Mvs$60/1M

Input price

$0.8/1Mvs$15/1M

Context window

200kvs200k

Claude 3.5 Haiku: 2WOpenAI: o1: 0W

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

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 / visionOpenAI: o1

Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
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	OpenAI: o1	Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Frequently Asked Questions

Which is better overall, Claude 3.5 Haiku or OpenAI: o1?

OpenAI: o1 has the higher preference Elo in our latest snapshot (1,512). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, Claude 3.5 Haiku or OpenAI: o1?

OpenAI: o1 leads SWE-bench at 48.9% vs 36.2%. 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 $60/1M. Input prices and retry rates still move the real bill.

Which is faster, Claude 3.5 Haiku or OpenAI: o1?

Claude 3.5 Haiku has the lower time-to-first-token (110 ms vs 420 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 200k.

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 OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.