Pairwise benchmark snapshot · Aug 16, 2026

Doubao Pro 1.5 vs OpenAI: o1 benchmark

Name: Doubao Pro 1.5 vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Doubao Pro 1.5 (ByteDance) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 42 points (1,512 vs 1,470; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Doubao Pro 1.5 resolves 62.1% versus 48.9% (seed-bootstrap (Aug 1, 2026)). Doubao Pro 1.5 streams faster (105 tok/s) while Doubao Pro 1.5 is the cheaper output token ($0.28/1M). OpenAI: o1 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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ByteDance

Doubao Pro 1.5

ByteDance flagship enterprise model with ultra-low token cost and 128k context.

Elo 1,470$0.28/1M 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.

Doubao Pro 1.5 (5)vsOpenAI: o1 (5)

Doubao Pro 1.5: 5W (50%)Overall: Even matchupOpenAI: o1: 5W (50%)

← Doubao Pro 1.5OpenAI: o1 →

OpenAI: o1 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,470vs1,512

Coding Elo

1,465vs1,485

SWE-bench

62.1%vs48.9%

LiveBench

64%vs65.4%

GPQA Diamond

77.5%vs78%

Doubao Pro 1.5: 1WOpenAI: o1: 4W

Doubao Pro 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

105 tok/svs45 tok/s

Time to first token

190 msvs420 ms

Doubao Pro 1.5: 2WOpenAI: o1: 0W

Doubao Pro 1.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$60/1M

Input price

$0.11/1Mvs$15/1M

Context window

128kvs200k

Doubao Pro 1.5: 2WOpenAI: o1: 1W

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 AdvantageEven 3–3

Preference Elo

OpenAI: o1

42 pts advantage

Throughput Speed

Doubao Pro 1.5

60 tok/s faster

Price Efficiency

Doubao Pro 1.5

$59.72/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,470

EloHigh is better

1,512+3%

Code Elo

High is better

1,465

Code EloHigh is better

1,485+1%

LiveBench

High is better

64%

LiveBenchHigh is better

65.4%+2%

SWE-bench

High is better

+27%62.1%

SWE-benchHigh is better

48.9%

GPQA

High is better

77.5%

GPQAHigh is better

78%+1%

TTFT

Low is better

+121%190 ms

TTFTLow is better

420 ms

Speed

High is better

+133%105 tok/s

SpeedHigh is better

45 tok/s

In $

Low is better

+13536%$0.11/1M

In $Low is better

$15/1M

Out $

Low is better

+21329%$0.28/1M

Out $Low is better

$60/1M

Context

High is better

128k

ContextHigh is better

200k+56%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 99% with Doubao Pro 1.5

Monthly Volume50M tokens/mo

Quick Presets:

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

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

Doubao Pro 1.5$8.05 / mo

In: $3.85Out: $4.20

OpenAI: o1$1,425.00 / mo

In: $525.00Out: $900.00

Estimated Cost Delta

Doubao Pro 1.5 is estimated to save $1,416.95/month ($17,003/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
Doubao Pro 1.5:Pick Doubao Pro 1.5 when repo-level coding accuracy is the constraint.
2
Doubao Pro 1.5:Pick Doubao Pro 1.5 when time-to-first-token matters more than peak Elo.
3
OpenAI: o1:Pick OpenAI: o1 as the default general assistant.

Recommended Workload Routing

Repo / coding agentsDoubao Pro 1.5

Higher SWE-bench (62.1%).

High-volume chatDoubao Pro 1.5

Lower output list price ($0.28/1M).

Voice / low-latency UIDoubao Pro 1.5

Lower TTFT (190 ms).

Long-document RAGOpenAI: o1

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	Doubao Pro 1.5	Higher SWE-bench (62.1%).
High-volume chat	Doubao Pro 1.5	Lower output list price ($0.28/1M).
Voice / low-latency UI	Doubao Pro 1.5	Lower TTFT (190 ms).
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	Both accept images. Defaulting to the higher-Elo side (OpenAI: o1).

Community Sentiment

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LIVE

ByteDance

Doubao Pro 1.5

Elo 1,470

LiveBench64%

SWE-bench62.1%

Speed105 tok/s

Output cost$0.28/1M

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, Doubao Pro 1.5 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, Doubao Pro 1.5 or OpenAI: o1?: Doubao Pro 1.5 leads SWE-bench at 62.1% vs 48.9%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Doubao Pro 1.5 output tokens are $0.28/1M versus $60/1M. Input prices and retry rates still move the real bill.
Which is faster, Doubao Pro 1.5 or OpenAI: o1?: Doubao Pro 1.5 has the lower time-to-first-token (190 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: OpenAI: o1 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. Doubao Pro 1.5 and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Doubao Pro 1.5 matchups →|All OpenAI: o1 matchups →

Doubao Pro 1.5 vs Doubao Lite 1.5 (previous seed)Doubao Pro 1.5 vs Seed 2.1 Turbo (next seed)OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)GPT-5.6 Terra vs OpenAI: o1 GPT-5 vs OpenAI: o1

Doubao Pro 1.5 vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Doubao Pro 1.5 (5)vsOpenAI: o1 (5)

Doubao Pro 1.5: 5W (50%)Overall: Even matchupOpenAI: o1: 5W (50%)

← Doubao Pro 1.5OpenAI: o1 →

OpenAI: o1 (4/5)

Intelligence & Reasoning

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

Preference Elo

1,470vs1,512

Coding Elo

1,465vs1,485

SWE-bench

62.1%vs48.9%

LiveBench

64%vs65.4%

GPQA Diamond

77.5%vs78%

Doubao Pro 1.5: 1WOpenAI: o1: 4W

Doubao Pro 1.5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

105 tok/svs45 tok/s

Time to first token

190 msvs420 ms

Doubao Pro 1.5: 2WOpenAI: o1: 0W

Doubao Pro 1.5 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$60/1M

Input price

$0.11/1Mvs$15/1M

Context window

128kvs200k

Doubao Pro 1.5: 2WOpenAI: o1: 1W

Recommended Workload Routing

Repo / coding agentsDoubao Pro 1.5

Higher SWE-bench (62.1%).

High-volume chatDoubao Pro 1.5

Lower output list price ($0.28/1M).

Voice / low-latency UIDoubao Pro 1.5

Lower TTFT (190 ms).

Long-document RAGOpenAI: o1

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	Doubao Pro 1.5	Higher SWE-bench (62.1%).
High-volume chat	Doubao Pro 1.5	Lower output list price ($0.28/1M).
Voice / low-latency UI	Doubao Pro 1.5	Lower TTFT (190 ms).
Long-document RAG	OpenAI: o1	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, Doubao Pro 1.5 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, Doubao Pro 1.5 or OpenAI: o1?

Doubao Pro 1.5 leads SWE-bench at 62.1% vs 48.9%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Doubao Pro 1.5 output tokens are $0.28/1M versus $60/1M. Input prices and retry rates still move the real bill.

Which is faster, Doubao Pro 1.5 or OpenAI: o1?

Doubao Pro 1.5 has the lower time-to-first-token (190 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

OpenAI: o1 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. Doubao Pro 1.5 and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.