Gemini 2.5 Computer Use preview 10 2025

Google AI chat Deprecated 99d ago
Heads-up: Google AI has scheduled Gemini 2.5 Computer Use preview 10 2025 for deprecation on Jul 28, 2026. Plan a migration. Use Agent Command Center's model fallback routing to swap models without code changes.

Gemini 2.5 Computer Use preview 10 2025 is a Google AI chat model.It supports a 128,000-token context windowwith up to 64,000 output tokens.Input is priced at $1.25/M tokens and output at $10.00/M tokens. Capabilities include function calling, vision. Route Gemini 2.5 Computer Use preview 10 2025 via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate Gemini 2.5 Computer Use preview 10 2025 spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
064,000
5,000
01,000,000
Per request
$0.007750
in $0.003750 · out $0.004000
Per day
$38.75
5,000 requests
Per month
$1,179
152,188 requests

Estimate uses $1.25/M input · $10.00/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 $1.25/M
Output $10.00/M
Blended (3:1 in:out) $3.44/M
Cross-model comparable price

Context & limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
64,000 tokens
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • 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 Gemini 2.5 Computer Use preview 10 2025

Data-driven from Gemini 2.5 Computer Use preview 10 2025's percentile within chat peers.

Where it's strong

  • +pricing — cheaper than 76% of priced chat models on Future AGI

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops
  • !already deprecated — provider stopped accepting new traffic 99 days ago

Benchmarks pending

We haven't logged public benchmark scores for Gemini 2.5 Computer Use preview 10 2025 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Gemini 2.5 Computer Use preview 10 2025 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.

SDK
Native Future AGI client (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.
# Gemini 2.5 Computer Use preview 10 2025 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="google/gemini-2-5-computer-use-preview-10-2025",
    messages=[{"role": "user", "content": "Hello, Gemini 2.5 Computer Use preview 10 2025!"}],
)

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="google/gemini-2-5-computer-use-preview-10-2025",
    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"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗

Same model on other providers

gemini-2-5-computer-use-preview-10-2025 is also available via 1 other route. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google Vertex AI$1.25/M$10.00/MOct 22, 2026

Explore similar models

Gemini 2.5 Computer Use preview 10 2025 doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark. Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does Gemini 2.5 Computer Use preview 10 2025 cost?

Input is priced at $1.25 per 1M tokens and output at $10.00 per 1M tokens (Google AI, last verified Oct 22, 2026).

What is the context window of Gemini 2.5 Computer Use preview 10 2025?

Gemini 2.5 Computer Use preview 10 2025 supports a 128,000-token context window with up to 64,000 output tokens.

Does Gemini 2.5 Computer Use preview 10 2025 support function calling?

Yes — Gemini 2.5 Computer Use preview 10 2025 supports function (tool) calling.

Is Gemini 2.5 Computer Use preview 10 2025 good for production?

Gemini 2.5 Computer Use preview 10 2025 is well-suited for pricing — cheaper than 76% of priced chat models on Future AGI. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Gemini 2.5 Computer Use preview 10 2025 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 Gemini 2.5 Computer Use preview 10 2025

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.