Gemini 2.5 Pro

GitHub Copilot chat

Gemini 2.5 Pro is a GitHub Copilot chat model.It supports a 128,000-token context windowwith up to 64,000 output tokens. Capabilities include function calling, vision. Route Gemini 2.5 Pro via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: unknown Last verified: May 12, 2026 View source ↗
Pricing not yet public

We don't have verified per-token pricing for Gemini 2.5 Pro yet. If you have a source from GitHub Copilot's documentation, help us add it — your submission gets reviewed within 48 hours.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified May 12, 2026.

Input
Output

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 ✓ supported
  • 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

Where it's strong

  • +parallel tool calls — only 21% of chat models on Future AGI advertise this

Watch out for

  • !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.

MATH-500math· 0-shot CoT
Captured May 12, 2026
HumanEvalcode· 0-shot
Captured May 12, 2026
Chatbot Arena ELOgeneral· overall↑0% vs peers
Captured May 12, 2026
AIME 2025math· 0-shot
Captured May 12, 2026
MMLU-Proreasoning· 0-shot
Captured May 12, 2026
GPQA Diamondreasoning· 0-shot CoT
Captured May 12, 2026
MMMUmultimodal· 0-shot↑7% vs peers
Captured May 12, 2026
BFCL v3agent· multi-turn
Captured May 12, 2026
Aider Polyglotcode· pass@1
Captured May 12, 2026
LiveCodeBenchcode· pass@1
Captured May 12, 2026
SWE-bench Verifiedagent· agentic↑68% vs peers
Captured May 12, 2026
Humanity's Last Examreasoning· 0-shot
Captured May 12, 2026
Try it

Call Gemini 2.5 Pro 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 Pro 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="github-copilot/gemini-2-5-pro",
    messages=[{"role": "user", "content": "Hello, Gemini 2.5 Pro!"}],
)

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="github-copilot/gemini-2-5-pro",
    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 ↗
Advanced: fallback + cache config (YAML)
strategy: cost-optimized
targets:
  - model: gemini-2-5-pro
    provider: github-copilot
    weight: 80
fallbacks:
  - model: claude-opus-4-6
    provider: azure-ai-foundry
  - model: claude-opus-4-6-20260205
    provider: anthropic
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Same model on other providers

gemini-2-5-pro is also available via 2 other routes. Pricing, regions, and capabilities can differ — compare before routing production traffic.

ProviderInput / 1MOutput / 1MVerified
Google Vertex AI$1.25/M$10.00/MMay 12, 2026
Google AI$1.25/M$10.00/MMay 12, 2026

Compare with similar models

Grouped by Chatbot Arena tier (Gemini 2.5 Pro sits at 1448 ELO).

FAQ

How much does Gemini 2.5 Pro cost?

Public per-token pricing for Gemini 2.5 Pro is not yet published. Submit a source on this page to help us add it.

What is the context window of Gemini 2.5 Pro?

Gemini 2.5 Pro supports a 128,000-token context window with up to 64,000 output tokens.

Does Gemini 2.5 Pro support function calling?

Yes — Gemini 2.5 Pro supports function (tool) calling, including parallel tool calls.

Is Gemini 2.5 Pro good for production?

Gemini 2.5 Pro is well-suited for parallel tool calls — only 21% of chat models on Future AGI advertise this. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to Gemini 2.5 Pro 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 Pro

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