Model Comparison
DeepSeek-V4-Flash-0731 vs Gemma 3 27BWhich is better in 2026?
Comparing DeepSeek-V4-Flash-0731 and Gemma 3 27B across benchmarks, pricing, and capabilities.
Verdict: DeepSeek-V4-Flash-0731 vs Gemma 3 27B — which is better?
DeepSeek-V4-Flash-0731 (by DeepSeek) and Gemma 3 27B (by Google) 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.
On price, DeepSeek-V4-Flash-0731 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
DeepSeek-V4-Flash-0731 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose DeepSeek-V4-Flash-0731 if…
- cost matters — it's about 1.1x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Gemma 3 27B if…
- you want predictable pricing at $0.10/M input and $0.20/M output
Performance Benchmarks
Comparative analysis across standard metrics
DeepSeek-V4-Flash-0731 and Gemma 3 27Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, DeepSeek-V4-Flash-0731 ($0.09/1M tokens) is 1.1x cheaper than Gemma 3 27B ($0.10/1M tokens).
For output processing, DeepSeek-V4-Flash-0731 ($0.18/1M tokens) is 1.1x cheaper than Gemma 3 27B ($0.20/1M tokens).
In conclusion, Gemma 3 27B is more expensive than DeepSeek-V4-Flash-0731.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Flash-0731 has 277.0B more parameters than Gemma 3 27B, making it 1025.9% larger.
Context Window
Maximum input and output token capacity
DeepSeek-V4-Flash-0731 accepts 1,048,576 input tokens compared to Gemma 3 27B's 131,072 tokens. Gemma 3 27B can generate longer responses up to 131,072 tokens, while DeepSeek-V4-Flash-0731 is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Gemma 3 27B supports multimodal inputs, whereas DeepSeek-V4-Flash-0731 does not.
Gemma 3 27B can handle both text and other forms of data like images, making it suitable for multimodal applications.
DeepSeek-V4-Flash-0731
Gemma 3 27B
License
Usage and distribution terms
DeepSeek-V4-Flash-0731 is licensed under MIT, while Gemma 3 27B uses Gemma.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Gemma
Open weights
Release Timeline
When each model was launched
DeepSeek-V4-Flash-0731 was released on 2026-07-31, while Gemma 3 27B was released on 2025-03-12.
DeepSeek-V4-Flash-0731 is 17 months newer than Gemma 3 27B.
Jul 31, 2026
6 days ago
1.4yr newerMar 12, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
DeepSeek-V4-Flash-0731 is available from DeepInfra, Fireworks, Novita. Gemma 3 27B is available from DeepInfra, Novita.
DeepSeek-V4-Flash-0731
Gemma 3 27B
Outputs Comparison
Key Takeaways
Gemma 3 27B
View detailsDetailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against DeepSeek-V4-Flash-0731 and Gemma 3 27B side-by-side, then vote on the output you prefer.
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FAQ
Common questions about DeepSeek-V4-Flash-0731 vs Gemma 3 27B.