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  3. DeepSeek: R1 Distill Llama 70B vs Gemini 2.0 Flash Thinking

Pairwise benchmark snapshot · Aug 17, 2026noindex (thin pair)

DeepSeek: R1 Distill Llama 70B vs Gemini 2.0 Flash Thinking benchmark

In this head-to-head showdown, 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: Choose DeepSeek: R1 Distill Llama 70B if your priority is peak reasoning, complex code generation, and top preference Elo. Choose Gemini 2.0 Flash Thinking if you are optimizing for low latency, high throughput, and cost-efficient API deployment.
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DeepSeek
DeepSeek: R1 Distill Llama 70B

Auto-discovered from OpenRouter (deepseek/deepseek-r1-distill-llama-70b). Preview until a second source matches.

$0.7999999999999999/1M out
Google
Gemini 2.0 Flash Thinking

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

Elo 1,490$0.4/1M out

Gemini 2.0 Flash Thinking Thinking Depth & Cost Scaler

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 Distill Llama 70B
All Matchups
Compare vs:vs Claude Opus 4.5vs GPT-5vs Gemini 3 Provs Grok 4.6vs Qwen 3 Max
Preference Leader

No shared data

Single model data

Throughput Leader

No shared data

No latency data

Value per Dollar Leader

Gemini 2.0 Flash Thinking

$0.4 / 1M output

Capability Percentiles

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

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 2.0 Flash Thinking 6/6
Head-to-Head Comparison

Overall Matchup Breakdown

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

DeepSeek: R1 Distill Llama 70B (0)vsGemini 2.0 Flash Thinking (10)
DeepSeek: R1 Distill Llama 70B: 0W (0%)Overall: Gemini 2.0 Flash ThinkingGemini 2.0 Flash Thinking: 10W (100%)
← DeepSeek: R1 Distill Llama 70BGemini 2.0 Flash Thinking →
Gemini 2.0 Flash Thinking (5/5)

Intelligence & Reasoning

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

Preference Elo
—vs1,490
Coding Elo
—vs1,515
SWE-bench
—vs66.8%
LiveBench
—vs67%
GPQA Diamond
—vs81%
DeepSeek: R1 Distill Llama 70B: 0WGemini 2.0 Flash Thinking: 5W
Gemini 2.0 Flash Thinking (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed
—vs115 tok/s
Time to first token
—vs220 ms
DeepSeek: R1 Distill Llama 70B: 0WGemini 2.0 Flash Thinking: 2W
Gemini 2.0 Flash Thinking (3/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price
$0.8/1Mvs$0.4/1M
Input price
$0.8/1Mvs$0.1/1M
Context window
8kvs1M
DeepSeek: R1 Distill Llama 70B: 0WGemini 2.0 Flash Thinking: 3W

Side-by-Side Benchmark Matrix

Preference Elo
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking1,490
lmarena · Aug 17, 2026
Coding Elo
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking1,515
lmarena · Aug 17, 2026
LiveBench
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking67%
livebench · Aug 17, 2026
SWE-bench
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking66.8%
swebench · Aug 17, 2026
GPQA Diamond
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking81%
seed-bootstrap · Aug 1, 2026
Time to first token
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking220 ms
seed-bootstrap · Aug 1, 2026
Output speed
DeepSeek: R1 Distill Llama 70B—
Gemini 2.0 Flash Thinking115 tok/s
seed-bootstrap · Aug 1, 2026
Input priceGemini 2.0 Flash Thinking (+$0.7/1M)
DeepSeek: R1 Distill Llama 70B$0.8/1M
openrouter · Aug 17, 2026
Gemini 2.0 Flash Thinking$0.1/1M
seed-bootstrap · Aug 1, 2026
Output priceGemini 2.0 Flash Thinking (+$0.4/1M)
DeepSeek: R1 Distill Llama 70B$0.8/1M
openrouter · Aug 17, 2026
Gemini 2.0 Flash Thinking$0.4/1M
seed-bootstrap · Aug 1, 2026
Context windowGemini 2.0 Flash Thinking (+1M)
DeepSeek: R1 Distill Llama 70B8k
openrouter · Aug 17, 2026
Gemini 2.0 Flash Thinking1M
seed-bootstrap · Aug 1, 2026
BenchmarkDeepSeek: R1 Distill Llama 70BGemini 2.0 Flash ThinkingAdvantage Delta
Preference Elo—
1,490
lmarena · Aug 17, 2026
—
Coding Elo—
1,515
lmarena · Aug 17, 2026
—
LiveBench—
67%
livebench · Aug 17, 2026
—
SWE-bench—
66.8%
swebench · Aug 17, 2026
—
GPQA Diamond—
81%
seed-bootstrap · Aug 1, 2026
—
Time to first token—
220 ms
seed-bootstrap · Aug 1, 2026
—
Output speed—
115 tok/s
seed-bootstrap · Aug 1, 2026
—
Input price
$0.8/1M
openrouter · Aug 17, 2026
$0.1/1M
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking (+$0.7/1M)
Output price
$0.8/1M
openrouter · Aug 17, 2026
$0.4/1M
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking (+$0.4/1M)
Context window
8k
openrouter · Aug 17, 2026
1M
seed-bootstrap · Aug 1, 2026
Gemini 2.0 Flash Thinking (+1M)
Interactive Simulator (USD)

Workload Cost & Savings Calculator

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

Save up to 76% 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 Distill Llama 70B$40.00 / mo
In: $28Out: $12
Gemini 2.0 Flash Thinking$9.50 / mo
In: $3.5Out: $6
Estimated Cost Delta

Gemini 2.0 Flash Thinking is estimated to save $30.50/month ($366/year).

Based on 50M tokens (30% gen)

Key Selection Recommendations

  • 1
    Gemini 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking when you are optimizing output cost.
  • 2
    Gemini 2.0 Flash Thinking:Pick Gemini 2.0 Flash Thinking for million-token RAG or long-document jobs.

Recommended Workload Routing

Repo / coding agentsinsufficient data

Need SWE-bench on both sides.

High-volume chatGemini 2.0 Flash Thinking

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

Voice / low-latency UIinsufficient data

Need TTFT on both sides.

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 agentsinsufficient dataNeed SWE-bench on both sides.
High-volume chatGemini 2.0 Flash ThinkingLower output list price ($0.4/1M).
Voice / low-latency UIinsufficient dataNeed TTFT on both sides.
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.
Community Sentiment

Cast Your Matchup Vote

0 total votes

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CompareLLM Matrix

Verified head-to-head card

LIVE
VS
DeepSeek

DeepSeek: R1 Distill Llama 70B

Elo n/a
LiveBench—
SWE-bench—
Speed—
Output cost$0.8/1M
Google

Gemini 2.0 Flash Thinking

Elo 1,490
LiveBench67%
SWE-bench66.8%
Speed115 tok/s
Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, DeepSeek: R1 Distill Llama 70B or Gemini 2.0 Flash Thinking?
We do not have preference Elo for both models, so we do not declare an overall winner. Compare the metrics that exist.
Which is better at coding, DeepSeek: R1 Distill Llama 70B or Gemini 2.0 Flash Thinking?
SWE-bench is missing for at least one model, so we do not rank coding from a single number.
Which is cheaper to run in production?
Gemini 2.0 Flash Thinking output tokens are $0.4/1M versus $0.8/1M. Input prices and retry rates still move the real bill.
Which is faster, DeepSeek: R1 Distill Llama 70B or Gemini 2.0 Flash Thinking?
We do not have TTFT for both models.
Which has the larger context window?
Gemini 2.0 Flash Thinking accepts 1M tokens versus 8k.
Can I self-host either model?
Neither model is marked open-weights here. You are comparing hosted APIs.
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 Distill Llama 70B and Gemini 2.0 Flash Thinking Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All DeepSeek: R1 Distill Llama 70B matchups →|All Gemini 2.0 Flash Thinking matchups →
Gemini 2.0 Flash Thinking vs Gemini 2.5 Flash (next gemini-flash)Gemini 3 Pro vs Gemini 2.0 Flash ThinkingGemini 3 Flash vs Gemini 2.0 Flash ThinkingGemini 3.6 Pro vs Gemini 2.0 Flash ThinkingGemini 3.6 Flash vs Gemini 2.0 Flash ThinkingGemini 3.7 Flash vs Gemini 2.0 Flash Thinking