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Gemini 2.5 Pro vs Llama 3.1 70B Instruct

Gemini 2.5 Pro leads the LLM Stats Score 27.8 to 7.6. Llama 3.1 70B Instruct is 17.2x cheaper per token.

Google · Meta · Updated for 2026

Which is better?

Gemini 2.5 Pro leads the overall LLM Stats Score 27.8 to 7.6, ranking #138 overall.

In the 1 individual benchmarks reported for both models, Gemini 2.5 Pro wins 1; this is a narrower head-to-head signal than the composite indexes.

On price, Llama 3.1 70B Instruct is roughly 17.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

Gemini 2.5 Pro also accepts a larger context window (1,000,000 input tokens), making it the stronger choice for long documents and large codebases.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemini 2.5 Pro

  • overall performance matters — it scores 27.8 and ranks #138 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you process long inputs — it offers a 1,000,000 token context window
  • you want the most recent training data — it shipped May 2025

Choose Llama 3.1 70B Instruct

  • cost matters — it's about 17.2x cheaper per token
  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Gemini 2.5 Pro
Google
Llama 3.1 70B Instruct
Meta
Core performance indexes
LLM Stats Score
27.8
#138
7.6
#286
Reasoning
27.3
#138
6.3
#288
Coding
12.8
#141
3.6
#215
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
$1.25 / M
$0.20 / M
Output price
$10.00 / M
$0.20 / M
Context window
1,000,000
128,000

Capability indexes

Additional strengths measured across groups of related public benchmarks

1 shared
Index
Gemini 2.5 Pro
Llama 3.1 70B Instruct
Math
22.6#135
11.4#241
Conservative TrueSkill rating · higher is betterHow scores work

Individual benchmarks

16 reported for Gemini 2.5 Pro · 18 for Llama 3.1 70B Instruct

1 shared

Gemini 2.5 Pro outperforms in 1 benchmarks (GPQA), while Llama 3.1 70B Instruct is better at 0 benchmarks.

Gemini 2.5 Pro significantly outperforms across most benchmarks.

Mon Sep 21 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

Pricing Analysis

Price comparison per million tokens

Llama 3.1 70B Instruct costs less

For input processing, Gemini 2.5 Pro ($1.25/1M tokens) is 6.3x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

For output processing, Gemini 2.5 Pro ($10.00/1M tokens) is 50.0x more expensive than Llama 3.1 70B Instruct ($0.20/1M tokens).

In conclusion, Gemini 2.5 Pro is more expensive than Llama 3.1 70B Instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Mon Sep 21 2026 • llm-stats.com
Google
Gemini 2.5 Pro
Input tokens$1.25
Output tokens$10.00
Best providerDeepinfra
Meta
Llama 3.1 70B Instruct
Input tokens$0.20
Output tokens$0.20
Best providerLambda
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 2.5 Pro accepts 1,000,000 input tokens compared to Llama 3.1 70B Instruct's 128,000 tokens. Gemini 2.5 Pro can generate longer responses up to 1,000,000 tokens, while Llama 3.1 70B Instruct is limited to 128,000 tokens.

Google
Gemini 2.5 Pro
Input1,000,000 tokens
Output1,000,000 tokens
Meta
Llama 3.1 70B Instruct
Input128,000 tokens
Output128,000 tokens
Mon Sep 21 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

Gemini 2.5 Pro supports multimodal inputs, whereas Llama 3.1 70B Instruct does not.

Gemini 2.5 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.5 Pro

Text
Images
Audio
Video

Llama 3.1 70B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.5 Pro is licensed under a proprietary license, while Llama 3.1 70B Instruct uses Llama 3.1 Community License.

License differences may affect how you can use these models in commercial or open-source projects.

Gemini 2.5 Pro

Proprietary

Closed source

Llama 3.1 70B Instruct

Llama 3.1 Community License

Open weights

Release Timeline

When each model was launched

Gemini 2.5 Pro was released on 2025-05-20, while Llama 3.1 70B Instruct was released on 2024-07-23.

Gemini 2.5 Pro is 10 months newer than Llama 3.1 70B Instruct.

Gemini 2.5 Pro

May 20, 2025

1.3 years ago

10mo newer
Llama 3.1 70B Instruct

Jul 23, 2024

2.2 years ago

Knowledge Cutoff

When training data ends

Gemini 2.5 Pro has a documented knowledge cutoff of 2025-01-31, while Llama 3.1 70B Instruct's cutoff date is not specified.

We can confirm Gemini 2.5 Pro's training data extends to 2025-01-31, but cannot make a direct comparison without Llama 3.1 70B Instruct's cutoff date.

Gemini 2.5 Pro

Jan 2025

Llama 3.1 70B Instruct

—

Provider Availability

Gemini 2.5 Pro is available from DeepInfra, Google. Llama 3.1 70B Instruct is available from Lambda, DeepInfra, Hyperbolic, Groq, Cerebras, Together, Fireworks, Bedrock, Sambanova.

Gemini 2.5 Pro

deepinfra logo
Deepinfra
Input Price:Input: $1.25/1MOutput Price:Output: $10.00/1M
google logo
Google
Input Price:Input: $1.25/1MOutput Price:Output: $10.00/1M

Llama 3.1 70B Instruct

lambda logo
Lambda
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
deepinfra logo
Deepinfra
Input Price:Input: $0.35/1MOutput Price:Output: $0.40/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.40/1MOutput Price:Output: $0.40/1M
groq logo
Groq
Input Price:Input: $0.59/1MOutput Price:Output: $0.78/1M
cerebras logo
Cerebras
Input Price:Input: $0.60/1MOutput Price:Output: $0.60/1M
together logo
Together
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
bedrock logo
AWS Bedrock
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
sambanova logo
Sambanova
Input Price:Input: $5.00/1MOutput Price:Output: $10.00/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Gemini 2.5 Pro and Llama 3.1 70B Instruct side-by-side, then vote on the output you prefer.

Gemini 2.5 Pro
✓ Preferred
Llama 3.1 70B Instruct
Open in Playground

FAQ

Common questions about Gemini 2.5 Pro vs Llama 3.1 70B Instruct.

Which is better, Gemini 2.5 Pro or Llama 3.1 70B Instruct?

Gemini 2.5 Pro leads the LLM Stats Score 27.8 to 7.6. Gemini 2.5 Pro is made by Google and Llama 3.1 70B Instruct is made by Meta. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does Gemini 2.5 Pro compare to Llama 3.1 70B Instruct in benchmarks?

Gemini 2.5 Pro scores MRCR: 93.0%, AIME 2024: 92.0%, Global-MMLU-Lite: 88.6%, Video-MME: 84.8%, AIME 2025: 83.0%. Llama 3.1 70B Instruct scores GSM-8K (CoT): 95.1%, ARC-C: 94.8%, API-Bank: 90.0%, IFEval: 87.5%, Multilingual MGSM (CoT): 86.9%.

Is Gemini 2.5 Pro cheaper than Llama 3.1 70B Instruct?

Llama 3.1 70B Instruct is 6.3x cheaper for input tokens. Gemini 2.5 Pro costs $1.25/M input and $10.00/M output via deepinfra. Llama 3.1 70B Instruct costs $0.20/M input and $0.20/M output via lambda.

What are the context window sizes for Gemini 2.5 Pro and Llama 3.1 70B Instruct?

Gemini 2.5 Pro supports 1.0M tokens and Llama 3.1 70B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 2.5 Pro and Llama 3.1 70B Instruct?

Key differences include LLM Stats Score (27.8 vs 7.6), context window (1.0M vs 128K), input pricing ($1.25 vs $0.20/M), multimodal support (yes vs no), licensing (Proprietary vs Llama 3.1 Community License). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.5 Pro and Llama 3.1 70B Instruct?

Gemini 2.5 Pro is developed by Google and Llama 3.1 70B Instruct is developed by Meta.

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