Model Comparison

Gemini 1.0 Pro vs DeepSeek-V3Which is better in 2026?

DeepSeek-V3 significantly outperforms across most benchmarks. DeepSeek-V3 is 1.6x cheaper per token.

Verdict: Gemini 1.0 Pro vs DeepSeek-V3 — which is better?

Gemini 1.0 Pro (by Google) and DeepSeek-V3 (by DeepSeek) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

Gemini 1.0 Pro outperforms in 0 benchmarks, while DeepSeek-V3 is better at 2 benchmarks (GPQA, MMLU). DeepSeek-V3 significantly outperforms across most benchmarks.

On price, DeepSeek-V3 is roughly 1.6x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.

DeepSeek-V3 also accepts a larger context window (131,072 input tokens), making it the stronger choice for long documents and large codebases.

Choose Gemini 1.0 Pro if…

  • you want predictable pricing at $0.50/M input and $1.50/M output

Choose DeepSeek-V3 if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • cost matters — it's about 1.6x cheaper per token
  • you process long inputs — it offers a 131,072 token context window
  • you want the most recent training data — it shipped Dec 2024
  • you need open weights you can self-host or fine-tune
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 1.0 Pro outperforms in 0 benchmarks, while DeepSeek-V3 is better at 2 benchmarks (GPQA, MMLU).

DeepSeek-V3 significantly outperforms across most benchmarks.

Wed Jun 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

DeepSeek-V3 costs less

For input processing, Gemini 1.0 Pro ($0.50/1M tokens) is 1.9x more expensive than DeepSeek-V3 ($0.27/1M tokens).

For output processing, Gemini 1.0 Pro ($1.50/1M tokens) is 1.4x more expensive than DeepSeek-V3 ($1.10/1M tokens).

In conclusion, Gemini 1.0 Pro is more expensive than DeepSeek-V3.*

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

Lowest available price from all providers
Wed Jun 17 2026 • llm-stats.com
Google
Gemini 1.0 Pro
Input tokens$0.50
Output tokens$1.50
Best providerGoogle
DeepSeek
DeepSeek-V3
Input tokens$0.27
Output tokens$1.10
Best providerDeepSeek
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

DeepSeek-V3 accepts 131,072 input tokens compared to Gemini 1.0 Pro's 32,760 tokens. DeepSeek-V3 can generate longer responses up to 131,072 tokens, while Gemini 1.0 Pro is limited to 8,192 tokens.

Google
Gemini 1.0 Pro
Input32,760 tokens
Output8,192 tokens
DeepSeek
DeepSeek-V3
Input131,072 tokens
Output131,072 tokens
Wed Jun 17 2026 • llm-stats.com

License

Usage and distribution terms

Gemini 1.0 Pro is licensed under a proprietary license, while DeepSeek-V3 uses MIT + Model License (Commercial use allowed).

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

Gemini 1.0 Pro

Proprietary

Closed source

DeepSeek-V3

MIT + Model License (Commercial use allowed)

Open weights

Release Timeline

When each model was launched

Gemini 1.0 Pro was released on 2024-02-15, while DeepSeek-V3 was released on 2024-12-25.

DeepSeek-V3 is 10 months newer than Gemini 1.0 Pro.

Gemini 1.0 Pro

Feb 15, 2024

2.3 years ago

DeepSeek-V3

Dec 25, 2024

1.5 years ago

10mo newer

Knowledge Cutoff

When training data ends

Gemini 1.0 Pro has a documented knowledge cutoff of 2024-02-01, while DeepSeek-V3's cutoff date is not specified.

We can confirm Gemini 1.0 Pro's training data extends to 2024-02-01, but cannot make a direct comparison without DeepSeek-V3's cutoff date.

Gemini 1.0 Pro

Feb 2024

DeepSeek-V3

—

Provider Availability

Gemini 1.0 Pro is available from Google. DeepSeek-V3 is available from DeepSeek.

Gemini 1.0 Pro

google logo
Google
Input Price:Input: $0.50/1MOutput Price:Output: $1.50/1M

DeepSeek-V3

deepseek logo
DeepSeek
Input Price:Input: $0.27/1MOutput Price:Output: $1.10/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google

No standout differentiators in the data we have for this pair.

DeepSeek
Larger context window (131,072 tokens)
Less expensive input tokens
Less expensive output tokens
Has open weights
Higher GPQA score (59.1% vs 27.9%)
Higher MMLU score (88.5% vs 71.8%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 1.0 Pro
DeepSeek
DeepSeek-V3

FAQ

Common questions about Gemini 1.0 Pro vs DeepSeek-V3.

Which is better, Gemini 1.0 Pro or DeepSeek-V3?

DeepSeek-V3 significantly outperforms across most benchmarks. Gemini 1.0 Pro is made by Google and DeepSeek-V3 is made by DeepSeek. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 1.0 Pro compare to DeepSeek-V3 in benchmarks?

Gemini 1.0 Pro scores FLEURS: 93.6%, BIG-Bench: 75.0%, MMLU: 71.8%, WMT23: 71.7%, EgoSchema: 55.7%. DeepSeek-V3 scores DROP: 91.6%, CLUEWSC: 90.9%, MATH-500: 90.2%, MMLU-Redux: 89.1%, MMLU: 88.5%.

Is Gemini 1.0 Pro cheaper than DeepSeek-V3?

DeepSeek-V3 is 1.9x cheaper for input tokens. Gemini 1.0 Pro costs $0.50/M input and $1.50/M output via google. DeepSeek-V3 costs $0.27/M input and $1.10/M output via deepseek.

What are the context window sizes for Gemini 1.0 Pro and DeepSeek-V3?

Gemini 1.0 Pro supports 33K tokens and DeepSeek-V3 supports 131K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 1.0 Pro and DeepSeek-V3?

Key differences include context window (33K vs 131K), input pricing ($0.50 vs $0.27/M), licensing (Proprietary vs MIT + Model License (Commercial use allowed)). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 1.0 Pro and DeepSeek-V3?

Gemini 1.0 Pro is developed by Google and DeepSeek-V3 is developed by DeepSeek.

Related comparisons