Pairwise benchmark snapshot · Aug 16, 2026

Gemini 2.0 Flash Thinking vs Llama 3.1 70B benchmark

Name: Gemini 2.0 Flash Thinking vs Llama 3.1 70B AI benchmark comparison
Creator: CompareLLM
License: https://creativecommons.org/licenses/by/4.0/

This page compares Gemini 2.0 Flash Thinking (Google) and Llama 3.1 70B (Meta) using the latest snapshots we have as of Aug 16, 2026. On preference Elo, Gemini 2.0 Flash Thinking is ahead by 250 points (1,490 vs 1,240; seed-bootstrap (Aug 1, 2026)). On SWE-bench coding, Gemini 2.0 Flash Thinking resolves 66.8% versus 40.2% (seed-bootstrap (Aug 1, 2026)). Llama 3.1 70B streams faster (125 tok/s) while Llama 3.1 70B is the cheaper output token ($0.4/1M). Gemini 2.0 Flash Thinking has the larger context window (1M 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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Google

Gemini 2.0 Flash Thinking

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

Elo 1,490$0.4/1M out

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Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 2.0 Flash Thinking (7)vsLlama 3.1 70B (3)

Gemini 2.0 Flash Thinking: 7W (70%)Overall: Gemini 2.0 Flash ThinkingLlama 3.1 70B: 3W (30%)

← Gemini 2.0 Flash ThinkingLlama 3.1 70B →

Gemini 2.0 Flash Thinking (5/5)

Intelligence & Reasoning

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

Preference Elo

1,490vs1,240

Coding Elo

1,515vs—

SWE-bench

66.8%vs40.2%

LiveBench

67%vs46.8%

GPQA Diamond

81%vs48%

Gemini 2.0 Flash Thinking: 5WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

115 tok/svs125 tok/s

Time to first token

220 msvs170 ms

Gemini 2.0 Flash Thinking: 0WLlama 3.1 70B: 2W

Gemini 2.0 Flash Thinking (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.4/1M

Input price

$0.1/1Mvs$0.4/1M

Context window

1Mvs131k

Gemini 2.0 Flash Thinking: 2WLlama 3.1 70B: 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 2.0 Flash Thinking 4/6

Preference Elo

Gemini 2.0 Flash Thinking

250 pts advantage

Throughput Speed

Llama 3.1 70B

10 tok/s faster

Price Efficiency

Llama 3.1 70B

$0.00/1M cheaper

Full Benchmark Score Matrix

—

Elo

High is better

+20%1,490

EloHigh is better

1,240

Code Elo

High is better

1,515

Code EloHigh is better

—

LiveBench

High is better

+43%67%

LiveBenchHigh is better

46.8%

SWE-bench

High is better

+66%66.8%

SWE-benchHigh is better

40.2%

GPQA

High is better

+69%81%

GPQAHigh is better

48%

TTFT

Low is better

220 ms

TTFTLow is better

170 ms+29%

Speed

High is better

115 tok/s

SpeedHigh is better

125 tok/s+9%

In $

Low is better

+300%$0.1/1M

In $Low is better

$0.4/1M

Out $

Low is better

$0.4/1M

Out $Low is better

$0.4/1M

Context

High is better

+700%1M

ContextHigh is better

131k

Interactive Simulator

Workload Cost & Savings Calculator

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

Save up to 52% 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)

Gemini 2.0 Flash Thinking$9.50 / mo

In: $3.50Out: $6.00

Llama 3.1 70B$20.00 / mo

In: $14.00Out: $6.00

Estimated Cost Delta

Gemini 2.0 Flash Thinking is estimated to save $10.50/month ($126/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
Llama 3.1 70B:Pick Llama 3.1 70B when you are optimizing output cost.
3
Llama 3.1 70B:Pick Llama 3.1 70B when time-to-first-token matters more than peak Elo.

Recommended Workload Routing

Repo / coding agentsGemini 2.0 Flash Thinking

Higher SWE-bench (66.8%).

High-volume chatLlama 3.1 70B

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

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 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 Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 2.0 Flash Thinking	Higher SWE-bench (66.8%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	Gemini 2.0 Flash Thinking	Larger window (1M).
Screenshots / vision	Gemini 2.0 Flash Thinking	Gemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

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LIVE

Google

Gemini 2.0 Flash Thinking

Elo 1,490

LiveBench67%

SWE-bench66.8%

Speed115 tok/s

Output cost$0.4/1M

Llama 3.1 70B

Elo 1,240

LiveBench46.8%

SWE-bench40.2%

Speed125 tok/s

Output cost$0.4/1M

Frequently Asked Questions

Which is better overall, Gemini 2.0 Flash Thinking or Llama 3.1 70B?: 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, Gemini 2.0 Flash Thinking or Llama 3.1 70B?: Gemini 2.0 Flash Thinking leads SWE-bench at 66.8% vs 40.2%. SWE-bench is one harness, not your repo.
Which is cheaper to run in production?: Llama 3.1 70B output tokens are $0.4/1M versus $0.4/1M. Input prices and retry rates still move the real bill.
Which is faster, Gemini 2.0 Flash Thinking or Llama 3.1 70B?: Llama 3.1 70B has the lower time-to-first-token (170 ms vs 220 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 131k.
Can I self-host either model?: Llama 3.1 70B 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. Gemini 2.0 Flash Thinking and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.

Similar Head-to-Head Comparisons

All Gemini 2.0 Flash Thinking matchups →|All Llama 3.1 70B matchups →

Gemini 2.0 Flash Thinking vs Gemini 2.5 Flash (next gemini-flash)Llama 3.1 70B vs Llama 3.1 405B (previous llama)Llama 3.1 70B vs Llama 3.3 70B (next llama)Gemini 3 Flash vs Gemini 2.0 Flash Thinking Gemini 3.6 Flash vs Gemini 2.0 Flash Thinking Llama 4 Maverick vs Gemini 2.0 Flash Thinking

Gemini 2.0 Flash Thinking vs Llama 3.1 70B benchmark

Interactive Comparison Diagram

Matchup Breakdown

Tug-of-War Matchup Matrix

Category wins across intelligence, speed, and pricing efficiency.

Gemini 2.0 Flash Thinking (7)vsLlama 3.1 70B (3)

Gemini 2.0 Flash Thinking: 7W (70%)Overall: Gemini 2.0 Flash ThinkingLlama 3.1 70B: 3W (30%)

← Gemini 2.0 Flash ThinkingLlama 3.1 70B →

Gemini 2.0 Flash Thinking (5/5)

Intelligence & Reasoning

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

Preference Elo

1,490vs1,240

Coding Elo

1,515vs—

SWE-bench

66.8%vs40.2%

LiveBench

67%vs46.8%

GPQA Diamond

81%vs48%

Gemini 2.0 Flash Thinking: 5WLlama 3.1 70B: 0W

Llama 3.1 70B (2/2)

Speed & Latency

Generation throughput and time to first token responsiveness

Output speed

115 tok/svs125 tok/s

Time to first token

220 msvs170 ms

Gemini 2.0 Flash Thinking: 0WLlama 3.1 70B: 2W

Gemini 2.0 Flash Thinking (2/3)

Pricing & Capacity

Cost per million tokens and max context window length

Output price

$0.4/1Mvs$0.4/1M

Input price

$0.1/1Mvs$0.4/1M

Context window

1Mvs131k

Gemini 2.0 Flash Thinking: 2WLlama 3.1 70B: 1W

Recommended Workload Routing

Repo / coding agentsGemini 2.0 Flash Thinking

Higher SWE-bench (66.8%).

High-volume chatLlama 3.1 70B

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

Voice / low-latency UILlama 3.1 70B

Lower TTFT (170 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 Workload	Recommended Pick	Evaluation Rationale
Repo / coding agents	Gemini 2.0 Flash Thinking	Higher SWE-bench (66.8%).
High-volume chat	Llama 3.1 70B	Lower output list price ($0.4/1M).
Voice / low-latency UI	Llama 3.1 70B	Lower TTFT (170 ms).
Long-document RAG	Gemini 2.0 Flash Thinking	Larger window (1M).
Screenshots / vision	Gemini 2.0 Flash Thinking	Gemini 2.0 Flash Thinking is the side marked multimodal in the catalog.

Frequently Asked Questions

Which is better overall, Gemini 2.0 Flash Thinking or Llama 3.1 70B?

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, Gemini 2.0 Flash Thinking or Llama 3.1 70B?

Gemini 2.0 Flash Thinking leads SWE-bench at 66.8% vs 40.2%. SWE-bench is one harness, not your repo.

Which is cheaper to run in production?

Llama 3.1 70B output tokens are $0.4/1M versus $0.4/1M. Input prices and retry rates still move the real bill.

Which is faster, Gemini 2.0 Flash Thinking or Llama 3.1 70B?

Llama 3.1 70B has the lower time-to-first-token (170 ms vs 220 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 131k.

Can I self-host either model?

Llama 3.1 70B 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. Gemini 2.0 Flash Thinking and Llama 3.1 70B Elo cells are dated snapshots from the named source — we do not compute Arena ourselves.