Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek R1 vs GPT-5 benchmark

Name: DeepSeek R1 vs GPT-5 AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek R1 (DeepSeek) and GPT-5 (OpenAI) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, GPT-5 is ahead by 200 points (1,558 vs 1,358; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, GPT-5 resolves 68.4% versus 65.2% (seed-bootstrap (Aug 1, 2026)). GPT-5 streams faster (78 tok/s) while DeepSeek R1 is the cheaper output token ($2.5/1M). GPT-5 has the larger context window (400k 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 R1

Open-weights reasoning model trained with large-scale RL.

Elo 1,358$2.5/1M out

OpenAI

GPT-5

OpenAI flagship reasoning model for 2025–26. Strong general preference Elo and multimodal coverage.

Elo 1,558$10/1M 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 R1 (2)vsGPT-5 (8)

DeepSeek R1: 2W (20%)Overall: GPT-5GPT-5: 8W (80%)

← DeepSeek R1GPT-5 →

GPT-5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,558

Coding Elo

—vs1,540

SWE-bench

65.2%vs68.4%

LiveBench

59%vs69.8%

GPQA Diamond

79.8%vs85.2%

DeepSeek R1: 0WGPT-5: 5W

GPT-5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs78 tok/s

Time to first token

420 msvs290 ms

DeepSeek R1: 0WGPT-5: 2W

DeepSeek R1 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$10/1M

Input price

$0.7/1Mvs$1.25/1M

Context window

64kvs400k

DeepSeek R1: 2WGPT-5: 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 AdvantageGPT-5 5/6

Preference Elo

GPT-5

200 pts advantage

Throughput Speed

GPT-5

16 tok/s faster

Price Efficiency

DeepSeek R1

$7.50/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,358

EloHigh is better

1,558+15%

Code Elo

High is better

—

Code EloHigh is better

1,540

LiveBench

High is better

59%

LiveBenchHigh is better

69.8%+18%

SWE-bench

High is better

65.2%

SWE-benchHigh is better

68.4%+5%

GPQA

High is better

79.8%

GPQAHigh is better

85.2%+7%

TTFT

Low is better

420 ms

TTFTLow is better

290 ms+45%

Speed

High is better

62 tok/s

SpeedHigh is better

78 tok/s+26%

In $

Low is better

+79%$0.7/1M

In $Low is better

$1.25/1M

Out $

Low is better

+300%$2.5/1M

Out $Low is better

$10/1M

Context

High is better

64k

ContextHigh is better

400k+525%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 68% with DeepSeek R1

Monthly Volume50M tokens/mo

Quick Presets:

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

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

DeepSeek R1$62.00 / mo

In: $24.50Out: $37.50

GPT-5$193.75 / mo

In: $43.75Out: $150.00

Estimated Cost Delta

DeepSeek R1 is estimated to save $131.75/month ($1,581/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

1
GPT-5:Pick GPT-5 when repo-level coding accuracy is the constraint.
2
DeepSeek R1:Pick DeepSeek R1 when you are optimizing output cost.
3
GPT-5:Pick GPT-5 when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGPT-5

Higher SWE-bench (68.4%).

High-volume chatDeepSeek R1

Lower output list price ($2.5/1M).

Voice / low-latency UIGPT-5

Lower TTFT (290 ms).

Long-document RAGGPT-5

Larger window (400k).

Screenshots / visionGPT-5

GPT-5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-5	Higher SWE-bench (68.4%).
High-volume chat	DeepSeek R1	Lower output list price ($2.5/1M).
Voice / low-latency UI	GPT-5	Lower TTFT (290 ms).
Long-document RAG	GPT-5	Larger window (400k).
Screenshots / vision	GPT-5	GPT-5 is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news

launchAug 8, 2025

GPT-5 (2025) remains the legacy OpenAI flagship baseline

Still a common compare target. 5.6 Sol replaced it as the buy; GPT-5 stays for old URLs.

launchJan 20, 2025

DeepSeek R1 stays as the open-weights RL baseline

Jan 2025 reasoning model. Still searched. V4 Pro is the newer DeepSeek buy.

Community Sentiment

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LIVE

DeepSeek

DeepSeek R1

Elo 1,358

LiveBench59%

SWE-bench65.2%

Speed62 tok/s

Output cost$2.5/1M

OpenAI

GPT-5

Elo 1,558

LiveBench69.8%

SWE-bench68.4%

Speed78 tok/s

Output cost$10/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GPT-5?: GPT-5 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek R1 or GPT-5?: GPT-5 leads SWE-bench at 68.4% vs 65.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: DeepSeek R1 output tokens are $2.5/1M versus $10/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or GPT-5?: GPT-5 has the lower time-to-first-token (290 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: GPT-5 accepts 400k tokens versus 64k.
Can I self-host either model?: DeepSeek R1 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 R1 and GPT-5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All GPT-5 matchups →

DeepSeek R1 vs DeepSeek V3 (previous deepseek)DeepSeek R1 vs DeepSeek V4 Pro (next deepseek)GPT-5 vs GPT-4.5 Orion (previous gpt-flagship)GPT-5 vs GPT-5.6 Sol (next gpt-flagship)DeepSeek V4 Flash vs GPT-5 DeepSeek V4 Pro vs GPT-5

DeepSeek R1 vs GPT-5 benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek R1 (2)vsGPT-5 (8)

DeepSeek R1: 2W (20%)Overall: GPT-5GPT-5: 8W (80%)

← DeepSeek R1GPT-5 →

GPT-5 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,358vs1,558

Coding Elo

—vs1,540

SWE-bench

65.2%vs68.4%

LiveBench

59%vs69.8%

GPQA Diamond

79.8%vs85.2%

DeepSeek R1: 0WGPT-5: 5W

GPT-5 (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

62 tok/svs78 tok/s

Time to first token

420 msvs290 ms

DeepSeek R1: 0WGPT-5: 2W

DeepSeek R1 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$2.5/1Mvs$10/1M

Input price

$0.7/1Mvs$1.25/1M

Context window

64kvs400k

DeepSeek R1: 2WGPT-5: 1W

Recommended Workload Routing

Repo / coding agentsGPT-5

Higher SWE-bench (68.4%).

High-volume chatDeepSeek R1

Lower output list price ($2.5/1M).

Voice / low-latency UIGPT-5

Lower TTFT (290 ms).

Long-document RAGGPT-5

Larger window (400k).

Screenshots / visionGPT-5

GPT-5 is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	GPT-5	Higher SWE-bench (68.4%).
High-volume chat	DeepSeek R1	Lower output list price ($2.5/1M).
Voice / low-latency UI	GPT-5	Lower TTFT (290 ms).
Long-document RAG	GPT-5	Larger window (400k).
Screenshots / vision	GPT-5	GPT-5 is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek R1 or GPT-5?

GPT-5 has the higher preference Elo in our latest snapshot (1,558). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek R1 or GPT-5?

GPT-5 leads SWE-bench at 68.4% vs 65.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

DeepSeek R1 output tokens are $2.5/1M versus $10/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek R1 or GPT-5?

GPT-5 has the lower time-to-first-token (290 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

GPT-5 accepts 400k tokens versus 64k.

Can I self-host either model?

DeepSeek R1 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 R1 and GPT-5 Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GPT-5

GPT-5

DeepSeek R1

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GPT-5 (2025) remains the legacy OpenAI flagship baseline

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

DeepSeek R1

GPT-5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons

Swap or Add Any Model to this Matchup

Interactive Comparison Diagram

Tug-of-War Matchup Matrix

Intelligence & Reasoning

Speed & Latency

Pricing & Capacity

Multi-Dimensional Capability Radar

GPT-5

GPT-5

DeepSeek R1

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

GPT-5 (2025) remains the legacy OpenAI flagship baseline

DeepSeek R1 stays as the open-weights RL baseline

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

DeepSeek R1

GPT-5

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons