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Every score has a dated snapshot.

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  3. DeepSeek R1 vs Gemini 2.0 Flash Thinking

Pairwise benchmark snapshot · Aug 1, 2026

DeepSeek R1 vs Gemini 2.0 Flash Thinking benchmark

In this head-to-head showdown, Gemini 2.0 Flash Thinking delivers higher overall intelligence and human-preferred responses (Elo 1,490 vs 1,358), while Gemini 2.0 Flash Thinking leads in SWE-bench software engineering benchmarks (66.8%), while Gemini 2.0 Flash Thinking is more budget-friendly at $0.4/1M per 1M output tokens. Review the complete breakdown below to determine which model best fits your performance and budget requirements.

Executive Comparison Verdict

Bottom Line: Gemini 2.0 Flash Thinking holds the advantage over DeepSeek R1 in overall benchmark performance and human evaluation rankings. Choose Gemini 2.0 Flash Thinking for demanding workloads, or review the breakdown below to compare coding and pricing metrics.
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Verified Head-to-Head Advantage Breakdown

9 direct benchmark disciplines evaluated across capability, speed, and cost

DeepSeek R1 (0)Gemini 2.0 Flash Thinking (9)
DeepSeek R1
0 of 9 Wins
Competitive baseline across remaining benchmarks
Gemini 2.0 Flash Thinking
9 of 9 Wins
✓Preference Elo✓LiveBench✓SWE-bench✓GPQA Diamond✓Time to first token✓Output speed✓Input price✓Output price✓Context window
DeepSeek
DeepSeek R1

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

Elo 1,358$2.19/1M out
Google
Leads 9 of 9 metrics
Gemini 2.0 Flash Thinking

Google experimental reasoning model that visualizes thoughts in real-time.

Elo 1,490$0.4/1M out

DeepSeek R1 Thinking Depth & Cost Scaler

Model A

Simulate accuracy gains vs added response time & token cost

Mode: Medium (Full RL CoT)

Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.

Thinking Delay
420 ms
Standard speed
Reasoning Depth
~2,800
Internal tokens
Cost / 1k Calls
$7.06
7.6× standard bill
Accuracy Boost
+6.5% accuracy
STEM & SWE-bench

Gemini 2.0 Flash Thinking Thinking Depth & Cost Scaler

Model B

Simulate accuracy gains vs added response time & token cost

Mode: Medium (8k tokens)

Reasoning models “think before answering” by generating internal reasoning tokens. Higher effort improves math, coding, and logic accuracy, but increases response delay and token costs.

Thinking Delay
220 ms
Standard speed
Reasoning Depth
~2,400
Internal tokens
Cost / 1k Calls
$1.13
6.6× standard bill
Accuracy Boost
+4.8% accuracy
STEM & SWE-bench
Top Rival Showdowns for DeepSeek R1
All Matchups
Compare vs:vs Claude Opus 4.5vs GPT-5vs Gemini 3 Provs Grok 4.6vs Qwen 3 Max
Preference LeaderGemini 2.0 Flash Thinking Wins

Gemini 2.0 Flash Thinking

Δ 132 Arena Elo pts

Throughput LeaderGemini 2.0 Flash Thinking Wins

Gemini 2.0 Flash Thinking

115 tok/s

Value per Dollar LeaderGemini 2.0 Flash Thinking Wins

Gemini 2.0 Flash Thinking

$0.4 / 1M output

Capability Percentiles

Relative percentile scores computed across all active models in the benchmark catalog.

Multi-Dimensional Capability Radar

Leaders Matchup Capability Radar

Comparing top Western standard models with China's leading frontier rival across 6 skill dimensions. Tap any spoke or dot to inspect.

Tap any node to inspect
Percentile 0–100
Head-to-Head Comparison

Overall Matchup Breakdown

Category wins across reasoning intelligence, generation speed, and token cost.

DeepSeek R1 (0)vsGemini 2.0 Flash Thinking (10)
DeepSeek R1: 0W (0%)Overall: Gemini 2.0 Flash ThinkingGemini 2.0 Flash Thinking: 10W (100%)
← DeepSeek R1Gemini 2.0 Flash Thinking →
🏆Gemini 2.0 Flash Thinking(5/5)

Intelligence & Reasoning

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

Benchmark
DeepSeek R1vsGemini 2.0 Flash Thinking
Preference Elo
1,358vs1,490+132
Coding Elo
—vs1,515
SWE-bench
65.2%vs66.8%+1.6%
LiveBench
59%vs67%+8%
GPQA Diamond
79.8%vs81%+1.2%
DeepSeek R1: 0WGemini 2.0 Flash Thinking: 5W
🏆Gemini 2.0 Flash Thinking(2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Benchmark
DeepSeek R1vsGemini 2.0 Flash Thinking
Output speed
62 tok/svs115 tok/s+53 tok/s
Time to first token
420 msvs220 ms+200 ms
DeepSeek R1: 0WGemini 2.0 Flash Thinking: 2W
🏆Gemini 2.0 Flash Thinking(3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Benchmark
DeepSeek R1vsGemini 2.0 Flash Thinking
Output price
$2.19/1Mvs$0.4/1M+$1.79/1M
Input price
$0.55/1Mvs$0.1/1M+$0.45/1M
Context window
128kvs1M+921k
DeepSeek R1: 0WGemini 2.0 Flash Thinking: 3W

Side-by-Side Benchmark Matrix

Preference Elo+132
DeepSeek R11,358
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking1,490
seed-bootstrap · Aug 1, 2026
Coding Elo
DeepSeek R1—
Gemini 2.0 Flash Thinking1,515
seed-bootstrap · Aug 1, 2026
LiveBench+8%
DeepSeek R159%
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking67%
seed-bootstrap · Aug 1, 2026
SWE-bench+1.6%
DeepSeek R165.2%
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking66.8%
seed-bootstrap · Aug 1, 2026
GPQA Diamond+1.2%
DeepSeek R179.8%
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking81%
seed-bootstrap · Aug 1, 2026
Time to first token+200 ms
DeepSeek R1420 ms
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking220 ms
seed-bootstrap · Aug 1, 2026
Output speed+53 tok/s
DeepSeek R162 tok/s
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking115 tok/s
seed-bootstrap · Aug 1, 2026
Input price+$0.45/1M
DeepSeek R1$0.55/1M
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking$0.1/1M
seed-bootstrap · Aug 1, 2026
Output price+$1.79/1M
DeepSeek R1$2.19/1M
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking$0.4/1M
seed-bootstrap · Aug 1, 2026
Context window+921k
DeepSeek R1128k
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking1M
seed-bootstrap · Aug 1, 2026
BenchmarkDeepSeek R1Gemini 2.0 Flash ThinkingAdvantage Delta
Preference Elo
1,358
seed-bootstrap · Aug 1, 2026
1,490
seed-bootstrap · Aug 1, 2026
+132
Coding Elo—
1,515
seed-bootstrap · Aug 1, 2026
—
LiveBench
59%
seed-bootstrap · Aug 1, 2026
67%
seed-bootstrap · Aug 1, 2026
+8%
SWE-bench
65.2%
seed-bootstrap · Aug 1, 2026
66.8%
seed-bootstrap · Aug 1, 2026
+1.6%
GPQA Diamond
79.8%
seed-bootstrap · Aug 1, 2026
81%
seed-bootstrap · Aug 1, 2026
+1.2%
Time to first token
420 ms
seed-bootstrap · Aug 1, 2026
220 ms
seed-bootstrap · Aug 1, 2026
+200 ms
Output speed
62 tok/s
seed-bootstrap · Aug 1, 2026
115 tok/s
seed-bootstrap · Aug 1, 2026
+53 tok/s
Input price
$0.55/1M
seed-bootstrap · Aug 1, 2026
$0.1/1M
seed-bootstrap · Aug 1, 2026
+$0.45/1M
Output price
$2.19/1M
seed-bootstrap · Aug 1, 2026
$0.4/1M
seed-bootstrap · Aug 1, 2026
+$1.79/1M
Context window
128k
seed-bootstrap · Aug 1, 2026
1M
seed-bootstrap · Aug 1, 2026
+921k
Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 82% with Gemini 2.0 Flash Thinking
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$52.10 / mo
In: $19.25Out: $32.85
Gemini 2.0 Flash Thinking$9.50 / mo
In: $3.5Out: $6
Estimated Cost Delta

Gemini 2.0 Flash Thinking is estimated to save $42.60/month ($511/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

  • 1
    Gemini 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking when repo-level coding accuracy is the constraint.
  • 2
    Gemini 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking when time-to-first-token matters more than peak Elo.
  • 3
    Gemini 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsGemini 2.0 Flash Thinking

Higher SWE-bench (66.8%).

High-volume chatGemini 2.0 Flash Thinking

Lower output list price ($0.4/1M).

Voice / low-latency UIGemini 2.0 Flash Thinking

Lower TTFT (220 ms).

Long-document RAGGemini 2.0 Flash Thinking

Larger window (1M).

Screenshots / visionGemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

Target WorkloadRecommended PickEvaluation Rationale
Repo / coding agentsGemini 2.0 Flash ThinkingHigher SWE-bench (66.8%).
High-volume chatGemini 2.0 Flash ThinkingLower output list price ($0.4/1M).
Voice / low-latency UIGemini 2.0 Flash ThinkingLower TTFT (220 ms).
Long-document RAGGemini 2.0 Flash ThinkingLarger window (1M).
Screenshots / visionGemini 2.0 Flash ThinkingGemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

Related Editorial Dispatches & Benchmark Notes

View all news
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.

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CompareLLM AI Matrix

Verified Head-to-Head Telemetry

LIVE BENCHMARK
VS
DeepSeek

DeepSeek R1

Elo 1,358
SWE-bench65.2%
Speed (tok/s)62 tok/s
Input / 1M Tokens$0.55/1M
Output / 1M Tokens$2.19/1M
Google

Gemini 2.0 Flash Thinking

Elo 1,490
SWE-bench66.8%
Speed (tok/s)115 tok/s
Input / 1M Tokens$0.1/1M
Output / 1M Tokens$0.4/1M

Frequently Asked Questions

Which is better overall, DeepSeek R1 or Gemini 2.0 Flash Thinking?
Gemini 2.0 Flash Thinking has the higher preference Elo in our latest snapshot (1,490). “Better” still depends on coding, price, and latency — see the table.
Which is better at coding, DeepSeek R1 or Gemini 2.0 Flash Thinking?
Gemini 2.0 Flash Thinking leads SWE-bench at 66.8% vs 65.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?
Gemini 2.0 Flash Thinking output tokens are $0.4/1M versus $2.19/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek R1 or Gemini 2.0 Flash Thinking?
Gemini 2.0 Flash Thinking has the lower time-to-first-token (220 ms vs 420 ms). Tokens/sec is a separate column if you care about long completions.
Which has the larger context window?
Gemini 2.0 Flash Thinking accepts 1M tokens versus 128k.
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 Gemini 2.0 Flash Thinking Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Related news

  • launch · Jan 20, 2025

    DeepSeek R1 stays as the open-weights RL baseline

Similar Head-to-Head Comparisons

All DeepSeek R1 matchups →|All Gemini 2.0 Flash Thinking matchups →
DeepSeek R1 vs DeepSeek V3 (previous deepseek)DeepSeek R1 vs DeepSeek V4 Pro (next deepseek)Gemini 2.0 Flash Thinking vs Gemini 2.5 Flash (next gemini-flash)DeepSeek V4 Flash vs Gemini 2.0 Flash ThinkingGemini 3 Flash vs Gemini 2.0 Flash ThinkingGemini 3.6 Flash vs Gemini 2.0 Flash Thinking