Pairwise benchmark snapshot · Aug 1, 2026

DeepSeek Coder V2 vs Gemini 1.5 Pro benchmark

Name: DeepSeek Coder V2 vs Gemini 1.5 Pro AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek Coder V2 (DeepSeek) and Gemini 1.5 Pro (Google) using the latest snapshots we have as of Aug 1, 2026. On preference Elo, DeepSeek Coder V2 is ahead by 105 points (1,365 vs 1,260; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, DeepSeek Coder V2 resolves 60.5% versus 38% (seed-bootstrap (Aug 1, 2026)). DeepSeek Coder V2 streams faster (75 tok/s) while DeepSeek Coder V2 is the cheaper output token ($0.28/1M). Gemini 1.5 Pro has the larger context window (2M 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

Google

Gemini 1.5 Pro

First million-token Gemini Pro. Baseline for 1.5 vs 2.5 vs 3.x Pro.

Elo 1,260$5/1M out

Swap or Add Any Model to this Matchup

Instant pairwise benchmark recalculation across 400+ LLMs

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 (8)vsGemini 1.5 Pro (2)

DeepSeek Coder V2: 8W (80%)Overall: DeepSeek Coder V2Gemini 1.5 Pro: 2W (20%)

← DeepSeek Coder V2Gemini 1.5 Pro →

DeepSeek Coder V2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,260

Coding Elo

1,442vs—

SWE-bench

60.5%vs38%

LiveBench

58.6%vs49.2%

GPQA Diamond

70.2%vs58%

DeepSeek Coder V2: 5WGemini 1.5 Pro: 0W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs70 tok/s

Time to first token

290 msvs240 ms

DeepSeek Coder V2: 1WGemini 1.5 Pro: 1W

DeepSeek Coder V2 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$5/1M

Input price

$0.14/1Mvs$1.25/1M

Context window

128kvs2M

DeepSeek Coder V2: 2WGemini 1.5 Pro: 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 AdvantageDeepSeek Coder V2 4/6

Preference Elo

DeepSeek Coder V2

105 pts advantage

Throughput Speed

DeepSeek Coder V2

5 tok/s faster

Price Efficiency

DeepSeek Coder V2

$4.72/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+8%1,365

EloHigh is better

1,260

Code Elo

High is better

1,442

Code EloHigh is better

—

LiveBench

High is better

+19%58.6%

LiveBenchHigh is better

49.2%

SWE-bench

High is better

+59%60.5%

SWE-benchHigh is better

38%

GPQA

High is better

+21%70.2%

GPQAHigh is better

58%

TTFT

Low is better

290 ms

TTFTLow is better

240 ms+21%

Speed

High is better

+7%75 tok/s

SpeedHigh is better

70 tok/s

In $

Low is better

+793%$0.14/1M

In $Low is better

$1.25/1M

Out $

Low is better

+1686%$0.28/1M

Out $Low is better

$5/1M

Context

High is better

128k

ContextHigh is better

2M+1463%

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 92% 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

Gemini 1.5 Pro$118.75 / mo

In: $43.75Out: $75.00

Estimated Cost Delta

DeepSeek Coder V2 is estimated to save $109.65/month ($1,316/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
Gemini 1.5 Pro:Pick Gemini 1.5 Pro when time-to-first-token matters more than peak Elo.
3
Gemini 1.5 Pro:Pick Gemini 1.5 Pro for million-token RAG or long-document jobs.

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 UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro 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	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

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CompareLLM Matrix

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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

Google

Gemini 1.5 Pro

Elo 1,260

LiveBench49.2%

SWE-bench38%

Speed70 tok/s

Output cost$5/1M

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 or Gemini 1.5 Pro?: DeepSeek Coder V2 has the higher preference Elo in our latest snapshot (1,365). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek Coder V2 or Gemini 1.5 Pro?: DeepSeek Coder V2 leads SWE-bench at 60.5% vs 38%. 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 $5/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek Coder V2 or Gemini 1.5 Pro?: Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 1.5 Pro accepts 2M 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 Gemini 1.5 Pro 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 Gemini 1.5 Pro matchups →

DeepSeek Coder V2 vs DeepSeek V3 (next deepseek)Gemini 1.5 Pro vs Gemini 2.5 Pro (next gemini-pro)Gemini 2.0 Flash Thinking vs DeepSeek Coder V2 DeepSeek V4 Flash vs DeepSeek Coder V2 Gemini 3 Flash vs DeepSeek Coder V2 Gemini 3.6 Flash vs DeepSeek Coder V2

DeepSeek Coder V2 vs Gemini 1.5 Pro benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek Coder V2 (8)vsGemini 1.5 Pro (2)

DeepSeek Coder V2: 8W (80%)Overall: DeepSeek Coder V2Gemini 1.5 Pro: 2W (20%)

← DeepSeek Coder V2Gemini 1.5 Pro →

DeepSeek Coder V2 (5/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,260

Coding Elo

1,442vs—

SWE-bench

60.5%vs38%

LiveBench

58.6%vs49.2%

GPQA Diamond

70.2%vs58%

DeepSeek Coder V2: 5WGemini 1.5 Pro: 0W

Tied (1/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs70 tok/s

Time to first token

290 msvs240 ms

DeepSeek Coder V2: 1WGemini 1.5 Pro: 1W

DeepSeek Coder V2 (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.28/1Mvs$5/1M

Input price

$0.14/1Mvs$1.25/1M

Context window

128kvs2M

DeepSeek Coder V2: 2WGemini 1.5 Pro: 1W

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 UIGemini 1.5 Pro

Lower TTFT (240 ms).

Long-document RAGGemini 1.5 Pro

Larger window (2M).

Screenshots / visionGemini 1.5 Pro

Gemini 1.5 Pro 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	Gemini 1.5 Pro	Lower TTFT (240 ms).
Long-document RAG	Gemini 1.5 Pro	Larger window (2M).
Screenshots / vision	Gemini 1.5 Pro	Gemini 1.5 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 or Gemini 1.5 Pro?

DeepSeek Coder V2 has the higher preference Elo in our latest snapshot (1,365). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek Coder V2 or Gemini 1.5 Pro?

DeepSeek Coder V2 leads SWE-bench at 60.5% vs 38%. 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 $5/1M. Input prices and retry rates still move the real bill.

Which is faster, DeepSeek Coder V2 or Gemini 1.5 Pro?

Gemini 1.5 Pro has the lower time-to-first-token (240 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 1.5 Pro accepts 2M 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 Gemini 1.5 Pro Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.