Gemma 4 31B (free)

Gemma 4 31B (free) by Google excels in the Coding category, where it ranks #40 with a composite score of 80/100. Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function... It is particularly strong in areas highlighted by its top benchmark performance and adoption metrics, making it suitable for both individual developers and enterprise teams looking for a reliable coding solution.

Gemma 4 31B (free) is priced at $0.00 per million input tokens and $0.00 per million output tokens (USD). Contact the provider for volume discounts and enterprise pricing. Pricing is competitive within the coding category and reflects the model's quality-to-cost ratio.

In the Coding category, Gemma 4 31B (free) holds rank #40 out of 334 models tracked. Its quality rank is #40 and adoption rank is #40. You can use our comparison tool at /compare to see detailed side-by-side metrics with specific alternatives. Key differentiators include its composite scoring across benchmarks, community sentiment, and real-world adoption rates.

Gemma 4 31B (free) has been evaluated across 6 different signals. Its strongest areas include Capabilities (83/100), Benchmarks (86/100), Pricing (100/100). These scores are derived from industry-standard benchmarks, community ratings, and real-world performance metrics. The composite score of 80/100 reflects a weighted combination of all tracked signals.

Gemma 4 31B (free) is available as a free, open-source model. You can run it locally or use it through various hosting providers. Some API providers may charge for hosted inference, so check individual provider pricing.

Gemma 4 31B (free) supports a 262K token context window (262,144 tokens total). That translates to roughly 196,608 words in a single prompt. This is large enough to process entire codebases, research papers, or long conversation histories in one shot.

Gemma 4 31B (free) can generate up to 33K output tokens (32,768 tokens) per response. That is roughly 24,576 words. This is enough for generating complete code files, detailed reports, or long-form content in a single response.

Gemma 4 31B (free) supports image understanding (vision), function/tool calling, structured JSON output, extended reasoning/chain-of-thought, streaming responses. Function calling lets you integrate it with external APIs and tools programmatically. Vision support means it can analyze images, screenshots, and diagrams alongside text. These capabilities determine which workflows and integrations the model can handle natively.

Yes, Gemma 4 31B (free) is an open-source model. You can download the weights, run it locally, fine-tune it for your use case, or deploy it on your own infrastructure. Many cloud providers also offer hosted versions if you prefer not to manage the infrastructure yourself. Self-hosting gives you full control over data privacy and eliminates per-token API costs.

Gemma 4 31B (free) was developed by Google. It was released on April 2, 2026. You can access it through Google's API or download the model weights directly. Check our provider page for all models from Google and how they compare against each other.

Pick Gemma 4 31B (free) when you need top-tier performance and can justify the cost for quality-critical tasks like production code generation, complex reasoning, or enterprise deployments. If your task is straightforward text completion or classification, a cheaper model might give you 90% of the quality at a fraction of the price. Run a quick benchmark on your actual use case before committing.

You can access Gemma 4 31B (free) through Google's API using standard HTTP requests or their official SDK. Most providers support OpenAI-compatible endpoints, so switching between models often requires changing just the model name in your API call. Streaming is supported for real-time token-by-token output. For production use, implement proper error handling, rate limiting, and cost monitoring.