Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek V3 vs OpenAI: o1 benchmark

Name: DeepSeek V3 vs OpenAI: o1 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek V3 (DeepSeek) and OpenAI: o1 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, OpenAI: o1 is ahead by 202 points (1,512 vs 1,310; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, OpenAI: o1 resolves 48.9% versus 48.6% (seed-bootstrap (Aug 1, 2026)). DeepSeek V3 streams faster (66 tok/s) while DeepSeek V3 is the cheaper output token ($1.03/1M). 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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DeepSeek

DeepSeek V3

Prior DeepSeek flagship. Baseline for v3 vs v4.

Elo 1,310$1.0287/1M 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 B

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V3 (4)vsOpenAI: o1 (6)

DeepSeek V3: 4W (40%)Overall: OpenAI: o1OpenAI: o1: 6W (60%)

← DeepSeek V3OpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,512

Coding Elo

—vs1,485

SWE-bench

48.6%vs48.9%

LiveBench

55.4%vs65.4%

GPQA Diamond

59.1%vs78%

DeepSeek V3: 0WOpenAI: o1: 5W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs45 tok/s

Time to first token

380 msvs420 ms

DeepSeek V3: 2WOpenAI: o1: 0W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$60/1M

Input price

$0.26/1Mvs$15/1M

Context window

164kvs200k

DeepSeek V3: 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 AdvantageOpenAI: o1 4/6

Preference Elo

OpenAI: o1

202 pts advantage

Throughput Speed

DeepSeek V3

21 tok/s faster

Price Efficiency

DeepSeek V3

$58.97/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,310

EloHigh is better

1,512+15%

Code Elo

High is better

—

Code EloHigh is better

1,485

LiveBench

High is better

55.4%

LiveBenchHigh is better

65.4%+18%

SWE-bench

High is better

48.6%

SWE-benchHigh is better

48.9%+1%

GPQA

High is better

59.1%

GPQAHigh is better

78%+32%

TTFT

Low is better

+11%380 ms

TTFTLow is better

420 ms

Speed

High is better

+47%66 tok/s

SpeedHigh is better

45 tok/s

In $

Low is better

+5728%$0.26/1M

In $Low is better

$15/1M

Out $

Low is better

+5733%$1.03/1M

Out $Low is better

$60/1M

Context

High is better

164k

ContextHigh is better

200k+22%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 98% with DeepSeek V3

Monthly Volume50M tokens/mo

Quick Presets:

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

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

DeepSeek V3$24.44 / mo

In: $9.01Out: $15.43

OpenAI: o1$1,425.00 / mo

In: $525.00Out: $900.00

Estimated Cost Delta

DeepSeek V3 is estimated to save $1,400.56/month ($16,807/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
DeepSeek V3:Pick DeepSeek V3 when you are optimizing output cost.
3
DeepSeek V3:Pick DeepSeek V3 when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

High-volume chatDeepSeek V3

Lower output list price ($1.03/1M).

Voice / low-latency UIDeepSeek V3

Lower TTFT (380 ms).

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	DeepSeek V3	Lower TTFT (380 ms).
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

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LIVE

DeepSeek

DeepSeek V3

Elo 1,310

LiveBench55.4%

SWE-bench48.6%

Speed66 tok/s

Output cost$1.03/1M

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, DeepSeek V3 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, DeepSeek V3 or OpenAI: o1?: OpenAI: o1 leads SWE-bench at 48.9% vs 48.6%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: DeepSeek V3 output tokens are $1.03/1M versus $60/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek V3 or OpenAI: o1?: DeepSeek V3 has the lower time-to-first-token (380 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 164k.
Can I self-host either model?: DeepSeek V3 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.
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. DeepSeek V3 and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek V3 matchups →|All OpenAI: o1 matchups →

DeepSeek V3 vs DeepSeek Coder V2 (previous deepseek)DeepSeek V3 vs DeepSeek R1 (next deepseek)OpenAI: o1 vs GPT-4o (previous gpt-flagship)OpenAI: o1 vs GPT-4.5 Orion (next gpt-flagship)GPT-5 vs OpenAI: o1 DeepSeek V4 Pro vs OpenAI: o1

DeepSeek V3 vs OpenAI: o1 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V3 (4)vsOpenAI: o1 (6)

DeepSeek V3: 4W (40%)Overall: OpenAI: o1OpenAI: o1: 6W (60%)

← DeepSeek V3OpenAI: o1 →

OpenAI: o1 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,512

Coding Elo

—vs1,485

SWE-bench

48.6%vs48.9%

LiveBench

55.4%vs65.4%

GPQA Diamond

59.1%vs78%

DeepSeek V3: 0WOpenAI: o1: 5W

DeepSeek V3 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs45 tok/s

Time to first token

380 msvs420 ms

DeepSeek V3: 2WOpenAI: o1: 0W

DeepSeek V3 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$60/1M

Input price

$0.26/1Mvs$15/1M

Context window

164kvs200k

DeepSeek V3: 2WOpenAI: o1: 1W

Recommended Workload Routing

Repo / coding agentsOpenAI: o1

Higher SWE-bench (48.9%).

High-volume chatDeepSeek V3

Lower output list price ($1.03/1M).

Voice / low-latency UIDeepSeek V3

Lower TTFT (380 ms).

Long-document RAGOpenAI: o1

Larger window (200k).

Screenshots / visionOpenAI: o1

OpenAI: o1 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI: o1	Higher SWE-bench (48.9%).
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	DeepSeek V3	Lower TTFT (380 ms).
Long-document RAG	OpenAI: o1	Larger window (200k).
Screenshots / vision	OpenAI: o1	OpenAI: o1 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek V3 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, DeepSeek V3 or OpenAI: o1?

OpenAI: o1 leads SWE-bench at 48.9% vs 48.6%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek V3 output tokens are $1.03/1M versus $60/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V3 or OpenAI: o1?

DeepSeek V3 has the lower time-to-first-token (380 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 164k.

Can I self-host either model?

DeepSeek V3 is marked open-weights in our catalog. The other side is a closed API. Check the provider license before you ship weights.

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. DeepSeek V3 and OpenAI: o1 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.