Google: Gemma 4 31B

google/gemma-4-31b-it

Modalities: text+image+video->text
In / out price: $0.09 / $0.34
Context: 262K
Released: Apr 2, 2026
Knowledge cutoff: —

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...

VisionVideoToolsReasoningStructured outputLong context

Supported parameters

frequency_penalty
Frequency penalty
Lowers the chance of a token in proportion to how often it already appeared.
include_reasoning
Include reasoning
Include a reasoning summary in the response.
logit_bias
Logit bias
Add or subtract from token ids to encourage or ban them.
logprobs
Logprobs
Return the log probability of each output token.
max_tokens
Max tokens
Upper bound on generated tokens. Output stops at the limit.
min_p
Min-p
Drops tokens below this probability threshold.
presence_penalty
Presence penalty
Lowers the chance of any token that has already appeared.
reasoning
Reasoning
Enable or configure reasoning. The fields differ by model.
repetition_penalty
Repetition penalty
Penalizes text that already appeared. Common on open models.
response_format
Response format
For example json_object or json_schema, to constrain the shape.
seed
Seed
A fixed seed helps reproduce sampling. It does not guarantee byte-identical output.
stop
Stop
Stop when these strings are produced. A string or an array of strings.
structured_outputs
Structured outputs
Generate a parseable structure that follows a JSON Schema.
temperature
Temperature
Sampling temperature. Lower is steadier, higher is more varied. Often 0 to 2.Default 1
tool_choice
Tool choice
Whether to call a tool: auto, none, or a named function.
tools
Tools
Function definitions the model may call via tool_calls.
top_k
Top-K
Sample only from the K most likely tokens.Default 64
top_logprobs
Top logprobs
Also return the most likely alternatives at each position.
top_p
Top-p
Sample only from tokens whose cumulative probability reaches top_p.Default 0.95

Supported parameters

frequency_penalty

Frequency penalty

Lowers the chance of a token in proportion to how often it already appeared.

include_reasoning

Include reasoning

Include a reasoning summary in the response.

logit_bias

Logit bias

Add or subtract from token ids to encourage or ban them.

logprobs

Logprobs

Return the log probability of each output token.

max_tokens

Max tokens

Upper bound on generated tokens. Output stops at the limit.

min_p

Min-p

Drops tokens below this probability threshold.

presence_penalty

Presence penalty

Lowers the chance of any token that has already appeared.

reasoning

Reasoning

Enable or configure reasoning. The fields differ by model.

repetition_penalty

Repetition penalty

Penalizes text that already appeared. Common on open models.

response_format

Response format

For example json_object or json_schema, to constrain the shape.

seed

Seed

A fixed seed helps reproduce sampling. It does not guarantee byte-identical output.

stop

Stop

Stop when these strings are produced. A string or an array of strings.

structured_outputs

Structured outputs

Generate a parseable structure that follows a JSON Schema.

temperature

Temperature

Sampling temperature. Lower is steadier, higher is more varied. Often 0 to 2.Default 1

tool_choice

Tool choice

Whether to call a tool: auto, none, or a named function.

tools

Tools

Function definitions the model may call via tool_calls.

top_k

Top-K

Sample only from the K most likely tokens.Default 64

top_logprobs

Top logprobs

Also return the most likely alternatives at each position.

top_p

Top-p

Sample only from tokens whose cumulative probability reaches top_p.Default 0.95