Pairwise benchmark snapshot · Aug 17, 2026

DeepSeek Coder V2 vs OpenAI o1-mini benchmark

Name: DeepSeek Coder V2 vs OpenAI o1-mini AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek Coder V2 (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 80 points (1,445 vs 1,365; lmarena (Aug 17, 2026)). On SWE-bench coding, DeepSeek Coder V2 resolves 60.5% versus 56.4% (swebench (Aug 17, 2026)). OpenAI o1-mini streams faster (85 tok/s) while DeepSeek Coder V2 is the cheaper output token ($0.28/1M). DeepSeek Coder V2 has the larger context window (128k 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 Coder V2

DeepSeek open-weight Mixture-of-Experts coding model supporting 338 programming languages and 128k context.

Elo 1,365$0.28/1M 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 Coder V2 (3)vsOpenAI o1-mini (6)

DeepSeek Coder V2: 3W (33%)Overall: OpenAI o1-miniOpenAI o1-mini: 6W (67%)

← DeepSeek Coder V21 tiesOpenAI o1-mini →

OpenAI o1-mini (4/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,445

Coding Elo

1,442vs1,468

SWE-bench

60.5%vs56.4%

LiveBench

58.6%vs62%

GPQA Diamond

70.2%vs76.8%

DeepSeek Coder V2: 1WOpenAI o1-mini: 4W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs85 tok/s

Time to first token

290 msvs280 ms

DeepSeek Coder V2: 0WOpenAI o1-mini: 2W

DeepSeek Coder V2 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$4.4/1M

Input price

$0.14/1Mvs$1.1/1M

Context window

128kvs128k

DeepSeek Coder V2: 2WOpenAI 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 3/6

Preference Elo

OpenAI o1-mini

80 pts advantage

Throughput Speed

OpenAI o1-mini

10 tok/s faster

Price Efficiency

DeepSeek Coder V2

$4.12/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,365

EloHigh is better

1,445+6%

Code Elo

High is better

1,442

Code EloHigh is better

1,468+2%

LiveBench

High is better

58.6%

LiveBenchHigh is better

62%+6%

SWE-bench

High is better

+7%60.5%

SWE-benchHigh is better

56.4%

GPQA

High is better

70.2%

GPQAHigh is better

76.8%+9%

TTFT

Low is better

290 ms

TTFTLow is better

280 ms+4%

Speed

High is better

75 tok/s

SpeedHigh is better

85 tok/s+13%

In $

Low is better

+686%$0.14/1M

In $Low is better

$1.1/1M

Out $

Low is better

+1471%$0.28/1M

Out $Low is better

$4.4/1M

Context

High is better

128k

ContextHigh is better

128k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 91% with DeepSeek Coder V2

Monthly Volume50M tokens/mo

Quick Presets:

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

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

DeepSeek Coder V2$9.10 / mo

In: $4.90Out: $4.20

OpenAI o1-mini$104.50 / mo

In: $38.50Out: $66.00

Estimated Cost Delta

DeepSeek Coder V2 is estimated to save $95.40/month ($1,145/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
DeepSeek Coder V2:Pick DeepSeek Coder V2 when repo-level coding accuracy is the constraint.
2
OpenAI o1-mini:Pick OpenAI o1-mini when time-to-first-token matters more than peak Elo.
3
OpenAI o1-mini:Pick OpenAI o1-mini as the default general assistant.

Recommended Workload Routing

Repo / coding agentsDeepSeek Coder V2

Higher SWE-bench (60.5%).

High-volume chatDeepSeek Coder V2

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

Voice / low-latency UIOpenAI o1-mini

Lower TTFT (280 ms).

Long-document RAGDeepSeek Coder V2

Larger window (128k).

Screenshots / visionOpenAI o1-mini

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek Coder V2	Higher SWE-bench (60.5%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	OpenAI o1-mini	Lower TTFT (280 ms).
Long-document RAG	DeepSeek Coder V2	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

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LIVE

DeepSeek

DeepSeek Coder V2

Elo 1,365

LiveBench58.6%

SWE-bench60.5%

Speed75 tok/s

Output cost$0.28/1M

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 Coder V2 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 Coder V2 or OpenAI o1-mini?: DeepSeek Coder V2 leads SWE-bench at 60.5% vs 56.4%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: DeepSeek Coder V2 output tokens are $0.28/1M versus $4.4/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek Coder V2 or OpenAI o1-mini?: OpenAI o1-mini has the lower time-to-first-token (280 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: DeepSeek Coder V2 accepts 128k tokens versus 128k.
Can I self-host either model?: DeepSeek Coder V2 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 Coder V2 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 Coder V2 matchups →|All OpenAI o1-mini matchups →

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

DeepSeek Coder V2 vs OpenAI o1-mini benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek Coder V2 (3)vsOpenAI o1-mini (6)

DeepSeek Coder V2: 3W (33%)Overall: OpenAI o1-miniOpenAI o1-mini: 6W (67%)

← DeepSeek Coder V21 tiesOpenAI o1-mini →

OpenAI o1-mini (4/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,445

Coding Elo

1,442vs1,468

SWE-bench

60.5%vs56.4%

LiveBench

58.6%vs62%

GPQA Diamond

70.2%vs76.8%

DeepSeek Coder V2: 1WOpenAI o1-mini: 4W

OpenAI o1-mini (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs85 tok/s

Time to first token

290 msvs280 ms

DeepSeek Coder V2: 0WOpenAI o1-mini: 2W

DeepSeek Coder V2 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$4.4/1M

Input price

$0.14/1Mvs$1.1/1M

Context window

128kvs128k

DeepSeek Coder V2: 2WOpenAI o1-mini: 0W

Recommended Workload Routing

Repo / coding agentsDeepSeek Coder V2

Higher SWE-bench (60.5%).

High-volume chatDeepSeek Coder V2

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

Voice / low-latency UIOpenAI o1-mini

Lower TTFT (280 ms).

Long-document RAGDeepSeek Coder V2

Larger window (128k).

Screenshots / visionOpenAI o1-mini

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

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	DeepSeek Coder V2	Higher SWE-bench (60.5%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	OpenAI o1-mini	Lower TTFT (280 ms).
Long-document RAG	DeepSeek Coder V2	Larger window (128k).
Screenshots / vision	OpenAI o1-mini	OpenAI o1-mini is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 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 Coder V2 or OpenAI o1-mini?

DeepSeek Coder V2 leads SWE-bench at 60.5% vs 56.4%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek Coder V2 output tokens are $0.28/1M versus $4.4/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek Coder V2 or OpenAI o1-mini?

OpenAI o1-mini has the lower time-to-first-token (280 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

DeepSeek Coder V2 accepts 128k tokens versus 128k.

Can I self-host either model?

DeepSeek Coder V2 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 Coder V2 and OpenAI o1-mini Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.