Gemma 4 31B model profile

Profile facts

Profile type

Model research profile

Provider or publisher

Google

Context window

262,144 tokens

Snapshot date

2026-09-06

Knowledge cutoff

Not disclosed

Reference catalog ID

google/gemma-4-31b-it

When to pick Gemma 4 31B

Gemma 4 31B is a reference profile: Google publishes it, Kendr documents it for comparison, and it carries no Kendr alias to call.

Each condition below is derived from this model's own values in the 2026-09-06 snapshot, compared only against models publishing the same field.

• Reach for it when the input includes images. The snapshot records image input, so screenshots, scans, and diagrams can be sent directly instead of described.
• Reach for it when the model has to call tools. Tool calling is recorded in the snapshot, so this route can drive an agent loop rather than only answer in prose.
• Look elsewhere when you need to call it through Kendr today. This is a research reference profile with no Kendr alias. The facts and evidence here are published for comparison; the model is not routable on Kendr.

Context, modalities, and identity

Gemma 4 31B accepts image, text, and video and returns text. The snapshot records prompt caching, reasoning controls, structured output, text generation, tool calling, video input, and vision as capabilities or interfaces.

Provider or publisher

Google

Context window

262,144 tokens

Maximum output

16,384 tokens

Input modalities

image, text, and video

Output modalities

text

Knowledge cutoff

Not disclosed

Model overview

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 calling, and multilingual support across 140+ languages. Strong on coding, reasoning, and document understanding tasks. Apache 2.0 license.

Reference model ID

google/gemma-4-31b-it

Canonical version

google/gemma-4-31b-it-20260402

Hugging Face ID

google/gemma-4-31B-it

Capabilities and supported controls

Capabilities and parameters are reported from the dated catalog and source records; their presence does not guarantee identical behavior across every provider route.

Supported parameters

frequency_penalty, include_reasoning, logit_bias, logprobs, max_tokens, min_p, presence_penalty, reasoning, repetition_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k, top_logprobs, top_p

• prompt caching
• reasoning controls
• structured output
• text generation
• tool calling
• video input
• vision

Dated pricing snapshot

The dated reference snapshot lists $0.09 input and $0.34 output per million tokens. Cached input is $0.012 per million tokens.

These are dated third-party reference prices, not Kendr prices or an availability offer. Routes, tiers, caching, region, and provider policy can change the landed price.

Price date

2026-09-06

Input

$0.09 per 1M tokens

Cached input

$0.012 per 1M tokens

Output

$0.34 per 1M tokens

Provider routes, performance, and uptime

The snapshot retains 15 provider endpoints. Provider prices, context limits, p50 performance, and uptime can differ by route and are not Kendr guarantees.

Active provider endpoints

13

Best p50 latency

0.10 s

Best p50 throughput

525.5 tok/s

Availability with routing

99.97% over the sampled window

Availability without routing

92.56% over the sampled window

Performance date

2026-09-06

Benchmark evidence and limitations

The 2026-09-06 snapshot includes 2 Artificial Analysis indexes and AutoExacto results for 20 provider observations. Publisher-reported launch results are labeled and are not presented as Kendr measurements; scores from different suites are not treated as interchangeable.

Third-party catalog fields normalized from the cited model dataset; benchmark methodology and coverage differ by source.

Coding Index

43.4

Agentic Index

6.8

AutoExacto coverage

20 provider observations over 32 days

AutoExacto provider benchmarks

Rolling provider observations from the cited third-party benchmark view. The lookback is 32 days; GPQA Diamond and TAU-Bench Airline measure different abilities and should not be blended into one score.

Popularity and market context

Gemma 4 31B ranked #42 in the cited trailing-7-day third-party catalog usage dataset as of 2026-09-06. Observed within the cited trailing-7-day third-party catalog usage dataset; this is not global AI market share.

Third-party catalog rank

#42

Global market share

Not inferred

Observation date

2026-09-06

Frequently asked questions

Sources and evidence dates

1. Gemma 4 31B third-party catalog record (catalog, checked 2026-09-06)
2. Third-party model catalog methodology (methodology, checked 2026-09-06)
3. Google official model documentation (primary, checked 2026-09-06)
4. Artificial Analysis capability indices methodology (benchmark, checked 2026-09-06)
5. Gemma 4 31B third-party catalog record (benchmark, checked 2026-09-06)