Pairwise benchmark snapshot · Aug 16, 2026

DeepSeek Coder V2 vs Gemini 3.6 Pro benchmark

Name: DeepSeek Coder V2 vs Gemini 3.6 Pro AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares DeepSeek Coder V2 (DeepSeek) and Gemini 3.6 Pro (Google) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Gemini 3.6 Pro is ahead by 205 points (1,570 vs 1,365; seed-bootstrap (Aug 16, 2026)). On SWE-bench coding, Gemini 3.6 Pro resolves 74.6% versus 60.5% (seed-bootstrap (Aug 16, 2026)). Gemini 3.6 Pro streams faster (102 tok/s) while DeepSeek Coder V2 is the cheaper output token ($0.28/1M). Gemini 3.6 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 3.6 Pro

Current Google Pro-class multimodal model. Long context, strong coding, billed like the 3.x Pro tier.

Elo 1,570$5/1M out

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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 (2)vsGemini 3.6 Pro (8)

DeepSeek Coder V2: 2W (20%)Overall: Gemini 3.6 ProGemini 3.6 Pro: 8W (80%)

← DeepSeek Coder V2Gemini 3.6 Pro →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,570

Coding Elo

1,442vs1,566

SWE-bench

60.5%vs74.6%

LiveBench

58.6%vs71.8%

GPQA Diamond

70.2%vs84.4%

DeepSeek Coder V2: 0WGemini 3.6 Pro: 5W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs102 tok/s

Time to first token

290 msvs205 ms

DeepSeek Coder V2: 0WGemini 3.6 Pro: 2W

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 3.6 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 AdvantageGemini 3.6 Pro 5/6

Preference Elo

Gemini 3.6 Pro

205 pts advantage

Throughput Speed

Gemini 3.6 Pro

27 tok/s faster

Price Efficiency

DeepSeek Coder V2

$4.72/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

1,365

EloHigh is better

1,570+15%

Code Elo

High is better

1,442

Code EloHigh is better

1,566+9%

LiveBench

High is better

58.6%

LiveBenchHigh is better

71.8%+23%

SWE-bench

High is better

60.5%

SWE-benchHigh is better

74.6%+23%

GPQA

High is better

70.2%

GPQAHigh is better

84.4%+20%

TTFT

Low is better

290 ms

TTFTLow is better

205 ms+41%

Speed

High is better

75 tok/s

SpeedHigh is better

102 tok/s+36%

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 3.6 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
Gemini 3.6 Pro:Pick Gemini 3.6 Pro when repo-level coding accuracy is the constraint.
2
DeepSeek Coder V2:Pick DeepSeek Coder V2 when you are optimizing output cost.
3
Gemini 3.6 Pro:Pick Gemini 3.6 Pro when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGemini 3.6 Pro

Higher SWE-bench (74.6%).

High-volume chatDeepSeek Coder V2

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

Voice / low-latency UIGemini 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Gemini 3.6 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 3.6 Pro	Higher SWE-bench (74.6%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	Gemini 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Gemini 3.6 Pro is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

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Claude vs Gemini: coding, Elo, and output price — not a brand mashup

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Gemini vs GPT: long context versus the OpenAI flagship invoice

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Gemini 3.6 Pro: Google’s current Pro-class long-context row

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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 3.6 Pro

Elo 1,570

LiveBench71.8%

SWE-bench74.6%

Speed102 tok/s

Output cost$5/1M

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 or Gemini 3.6 Pro?: Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek Coder V2 or Gemini 3.6 Pro?: Gemini 3.6 Pro leads SWE-bench at 74.6% vs 60.5%. 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 3.6 Pro?: Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?: Gemini 3.6 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 3.6 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 3.6 Pro matchups →

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

DeepSeek Coder V2 vs Gemini 3.6 Pro benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

DeepSeek Coder V2 (2)vsGemini 3.6 Pro (8)

DeepSeek Coder V2: 2W (20%)Overall: Gemini 3.6 ProGemini 3.6 Pro: 8W (80%)

← DeepSeek Coder V2Gemini 3.6 Pro →

Gemini 3.6 Pro (5/5)

Intelligence & Reasoning

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

Preference Elo

1,365vs1,570

Coding Elo

1,442vs1,566

SWE-bench

60.5%vs74.6%

LiveBench

58.6%vs71.8%

GPQA Diamond

70.2%vs84.4%

DeepSeek Coder V2: 0WGemini 3.6 Pro: 5W

Gemini 3.6 Pro (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

75 tok/svs102 tok/s

Time to first token

290 msvs205 ms

DeepSeek Coder V2: 0WGemini 3.6 Pro: 2W

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 3.6 Pro: 1W

Recommended Workload Routing

Repo / coding agentsGemini 3.6 Pro

Higher SWE-bench (74.6%).

High-volume chatDeepSeek Coder V2

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

Voice / low-latency UIGemini 3.6 Pro

Lower TTFT (205 ms).

Long-document RAGGemini 3.6 Pro

Larger window (2M).

Screenshots / visionGemini 3.6 Pro

Gemini 3.6 Pro is the side marked multimodal in the catalog.

Target Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 3.6 Pro	Higher SWE-bench (74.6%).
High-volume chat	DeepSeek Coder V2	Lower output list price ($0.28/1M).
Voice / low-latency UI	Gemini 3.6 Pro	Lower TTFT (205 ms).
Long-document RAG	Gemini 3.6 Pro	Larger window (2M).
Screenshots / vision	Gemini 3.6 Pro	Gemini 3.6 Pro is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, DeepSeek Coder V2 or Gemini 3.6 Pro?

Gemini 3.6 Pro has the higher preference Elo in our latest snapshot (1,570). “Better” still depends on coding, price, and latency — see the table.

Which is better at coding, DeepSeek Coder V2 or Gemini 3.6 Pro?

Gemini 3.6 Pro leads SWE-bench at 74.6% vs 60.5%. 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 3.6 Pro?

Gemini 3.6 Pro has the lower time-to-first-token (205 ms vs 290 ms). Tokens/sec is a separate column if you care about long completions.

Which has the larger context window?

Gemini 3.6 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 3.6 Pro 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

Gemini 3.6 Pro

Gemini 3.6 Pro

DeepSeek Coder V2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

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CompareLLM Matrix

DeepSeek Coder V2

Gemini 3.6 Pro

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

Gemini 3.6 Pro

Gemini 3.6 Pro

DeepSeek Coder V2

Full Benchmark Score Matrix

Workload Cost & Savings Calculator

Key Selection Recommendations

Recommended Workload Routing

Related Editorial Dispatches & Benchmark Notes

Gemini Flash vs Pro: when Flash is the whole product

Claude vs Gemini: coding, Elo, and output price — not a brand mashup

Gemini vs GPT: long context versus the OpenAI flagship invoice

Gemini 3.6 Pro: Google’s current Pro-class long-context row

Cast Your Matchup Vote

Community Discussions (0)

Share card

CompareLLM Matrix

DeepSeek Coder V2

Gemini 3.6 Pro

Frequently Asked Questions

Related news

Similar Head-to-Head Comparisons