Pairwise benchmark snapshot · Aug 17, 2026

DeepSeek V3 vs OpenAI o1-mini benchmark

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

This page compares DeepSeek V3 (DeepSeek) and OpenAI o1-mini (OpenAI) using the latest snapshots we have as of Aug 17, 2026. On preference Elo, OpenAI o1-mini is ahead by 135 points (1,445 vs 1,310; lmarena (Aug 17, 2026)). On SWE-bench coding, OpenAI o1-mini resolves 56.4% versus 48.6% (swebench (Aug 17, 2026)). OpenAI o1-mini streams faster (85 tok/s) while DeepSeek V3 is the cheaper output token ($1.03/1M). DeepSeek V3 has the larger context window (164k 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-mini

OpenAI high-speed, cost-effective reasoning model optimized for STEM, math, and code generation.

Elo 1,445$4.4/1M 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.

DeepSeek V3 (3)vsOpenAI o1-mini (7)

DeepSeek V3: 3W (30%)Overall: OpenAI o1-miniOpenAI o1-mini: 7W (70%)

← DeepSeek V3OpenAI o1-mini →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,445

Coding Elo

—vs1,468

SWE-bench

48.6%vs56.4%

LiveBench

55.4%vs62%

GPQA Diamond

59.1%vs76.8%

DeepSeek V3: 0WOpenAI o1-mini: 5W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs85 tok/s

Time to first token

380 msvs280 ms

DeepSeek V3: 0WOpenAI o1-mini: 2W

DeepSeek V3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$4.4/1M

Input price

$0.2574/1Mvs$1.1/1M

Context window

164kvs128k

DeepSeek V3: 3WOpenAI o1-mini: 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-mini 4/6

Preference Elo

OpenAI o1-mini

135 pts advantage

Throughput Speed

OpenAI o1-mini

19 tok/s faster

Token Price Efficiency

DeepSeek V3

$3.37/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,310

EloHigh is better

1,445+10%

Code Elo

High is better

—

Code EloHigh is better

1,468

LiveBench

High is better

55.4%

LiveBenchHigh is better

62%+12%

SWE-bench

High is better

48.6%

SWE-benchHigh is better

56.4%+16%

GPQA

High is better

59.1%

GPQAHigh is better

76.8%+30%

TTFT

Low is better

380 ms

TTFTLow is better

280 ms+36%

Speed

High is better

66 tok/s

SpeedHigh is better

85 tok/s+29%

In $

Low is better

+327%$0.26 / 1M

In $Low is better

$1.10 / 1M

Out $

Low is better

+328%$1.03 / 1M

Out $Low is better

$4.40 / 1M

Context

High is better

+28%164k

ContextHigh is better

128k

Interactive Simulator (USD)

Workload Cost & Savings Calculator

Simulate monthly production API costs in USD (US Dollar).

Save up to 77% 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-mini$104.50 / mo

In: $38.5Out: $66

Estimated Cost Delta

DeepSeek V3 is estimated to save $80.06/month ($961/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
OpenAI o1-mini:Pick OpenAI o1-mini when repo-level coding accuracy is the constraint.
2
DeepSeek V3:Pick DeepSeek V3 when you are optimizing output cost.
3
OpenAI o1-mini:Pick OpenAI o1-mini when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsOpenAI o1-mini

Higher SWE-bench (56.4%).

High-volume chatDeepSeek V3

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

Voice / low-latency UIOpenAI o1-mini

Lower TTFT (280 ms).

Long-document RAGDeepSeek V3

Larger window (164k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI o1-mini	Higher SWE-bench (56.4%).
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	OpenAI o1-mini	Lower TTFT (280 ms).
Long-document RAG	DeepSeek V3	Larger window (164k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini 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-mini

Elo 1,445

LiveBench62%

SWE-bench56.4%

Speed85 tok/s

Output cost$4.4/1M

Frequently Asked Questions

Which is better overall, DeepSeek V3 or OpenAI o1-mini?: OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek V3 or OpenAI o1-mini?: OpenAI o1-mini leads SWE-bench at 56.4% 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 $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek V3 or OpenAI o1-mini?: OpenAI o1-mini has the lower time-to-first-token (280 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: DeepSeek V3 accepts 164k tokens versus 128k.
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-mini 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-mini matchups →

DeepSeek V3 vs DeepSeek Coder V2 (previous deepseek)DeepSeek V3 vs DeepSeek R1 (next deepseek)OpenAI o1-mini vs GPT-4o mini (previous gpt-mini)OpenAI o1-mini vs OpenAI: o3 Mini (next gpt-mini)GPT-5 vs OpenAI o1-mini DeepSeek V4 Pro vs OpenAI o1-mini

DeepSeek V3 vs OpenAI o1-mini benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek V3 (3)vsOpenAI o1-mini (7)

DeepSeek V3: 3W (30%)Overall: OpenAI o1-miniOpenAI o1-mini: 7W (70%)

← DeepSeek V3OpenAI o1-mini →

OpenAI o1-mini (5/5)

Intelligence & Reasoning

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

Preference Elo

1,310vs1,445

Coding Elo

—vs1,468

SWE-bench

48.6%vs56.4%

LiveBench

55.4%vs62%

GPQA Diamond

59.1%vs76.8%

DeepSeek V3: 0WOpenAI o1-mini: 5W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

66 tok/svs85 tok/s

Time to first token

380 msvs280 ms

DeepSeek V3: 0WOpenAI o1-mini: 2W

DeepSeek V3 (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$1.03/1Mvs$4.4/1M

Input price

$0.2574/1Mvs$1.1/1M

Context window

164kvs128k

DeepSeek V3: 3WOpenAI o1-mini: 0W

Recommended Workload Routing

Repo / coding agentsOpenAI o1-mini

Higher SWE-bench (56.4%).

High-volume chatDeepSeek V3

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

Voice / low-latency UIOpenAI o1-mini

Lower TTFT (280 ms).

Long-document RAGDeepSeek V3

Larger window (164k).

Screenshots / visionOpenAI o1-mini

OpenAI o1-mini is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	OpenAI o1-mini	Higher SWE-bench (56.4%).
High-volume chat	DeepSeek V3	Lower output list price ($1.03/1M).
Voice / low-latency UI	OpenAI o1-mini	Lower TTFT (280 ms).
Long-document RAG	DeepSeek V3	Larger window (164k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek V3 or OpenAI o1-mini?

OpenAI o1-mini has the higher preference Elo in our latest snapshot (1,445). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek V3 or OpenAI o1-mini?

OpenAI o1-mini leads SWE-bench at 56.4% 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 $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek V3 or OpenAI o1-mini?

OpenAI o1-mini has the lower time-to-first-token (280 ms vs 380 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek V3 accepts 164k tokens versus 128k.

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-mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.