Embeddingsgigar

GigaChat embedding

Embeddingsgigar is a GigaChat embedding model.It supports a 4,096-token context window. Route Embeddingsgigar 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 Embeddingsgigar yet. If you have a source from GigaChat'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
4,096 tokens
Max input
4,096 tokens
Max output
Modalities
embedding, text

Capabilities

  • Function calling — not advertised
  • Parallel tool calls — not advertised
  • Vision input — not advertised
  • 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

Watch out for

  • !small context (under 16K tokens)

Benchmarks pending

We haven't logged public benchmark scores for Embeddingsgigar yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Embeddingsgigar 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.
# Embeddingsgigar 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="gigachat/embeddingsgigar",
    messages=[{"role": "user", "content": "Hello, Embeddingsgigar!"}],
)

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="gigachat/embeddingsgigar",
    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 ↗

Compare with similar models

Embeddingsgigar doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.

FAQ

How much does Embeddingsgigar cost?

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

What is the context window of Embeddingsgigar?

Embeddingsgigar supports a 4,096-token context window.

Does Embeddingsgigar support function calling?

Embeddingsgigar 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 Embeddingsgigar good for production?

Embeddingsgigar is best evaluated against your own production traces. Pipe traffic through Agent Command Center to compare it head-to-head against alternatives in shadow mode.

How can I route to Embeddingsgigar 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 Embeddingsgigar

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