Gemma 4 31B IT
SambaNova chatGemma 4 31B IT is a SambaNova chat model.It supports a 131,072-token context windowwith up to 131,072 output tokens.Input is priced at $0.380/M tokens and output at $1.15/M tokens. Capabilities include vision. Route Gemma 4 31B IT via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Gemma 4 31B IT spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.3800/M input · $1.15/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.
Pricing
Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.
| Input | $0.380/M | |
| Output | $1.15/M | |
| Blended (3:1 in:out) | $0.573/M Cross-model comparable price |
Context & limits
- Context window
- 131,072 tokens
- Max input
- 131,072 tokens
- Max output
- 131,072 tokens
- Modalities
- vision, text
Capabilities
- Function calling — not advertised
- Parallel tool calls — not advertised
- Vision input ✓ supported
- Audio input — not advertised
- Audio output — not advertised
- PDF input — not advertised
- Streaming ✓ supported
- Structured output — not advertised
- Prompt caching — not advertised
- Reasoning — not advertised
Strengths & caveats — when to pick Gemma 4 31B IT
Data-driven from Gemma 4 31B IT's percentile within chat peers.
Where it's strong
- +long-form generation — 131,072-token max output, top-12% of peers
Watch out for
- !agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback
- !strict structured output — no JSON-schema enforcement, expect retry loops
Benchmark scores
Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.
Call Gemma 4 31B IT via Agent Command Center
One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.
agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.# Gemma 4 31B IT via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC
client = AgentCC(
api_key=os.environ["AGENTCC_API_KEY"], # from app.futureagi.com → Settings → API Keys
base_url="https://gateway.futureagi.com/v1",
)
resp = client.chat.completions.create(
model="sambanova/gemma-4-31b-it",
messages=[{"role": "user", "content": "Hello, Gemma 4 31B IT!"}],
)
print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")
# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
model="sambanova/gemma-4-31b-it",
messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost: ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache: ", raw.headers.get("x-agentcc-cache"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
- model: gemma-4-31b-it
provider: sambanova
weight: 80
fallbacks:
- model: claude-opus-5-5
provider: azure-ai-foundry
- model: claude-fable-5
provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }Same model on other providers
gemma-4-31b-it is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.
| Provider | Input / 1M | Output / 1M | Verified |
|---|---|---|---|
| Google AI | — | — | Oct 22, 2026 |
Explore similar models
Grouped by Chatbot Arena tier (Gemma 4 31B IT sits at 1452 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.
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≥30 ELO higher
25–100 ELO lower, ≤50% of price
FAQ
How much does Gemma 4 31B IT cost?
Input is priced at $0.380 per 1M tokens and output at $1.15 per 1M tokens (SambaNova, last verified Oct 22, 2026).
What is the context window of Gemma 4 31B IT?
Gemma 4 31B IT supports a 131,072-token context window with up to 131,072 output tokens.
Does Gemma 4 31B IT support function calling?
Gemma 4 31B IT does not currently advertise function-calling support. For agentic workloads, prefer a tool-calling-capable model and route via Agent Command Center for fallback.
Is Gemma 4 31B IT good for production?
Gemma 4 31B IT is well-suited for long-form generation — 131,072-token max output, top-12% of peers. Consider alternatives if you need agentic workflows — no advertised function-calling; use a tool-capable model and route via Agent Command Center for fallback.
How can I route to Gemma 4 31B IT with fallback?
Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.
Useful links for Gemma 4 31B IT
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.