Jamba Instruct v1.0

Amazon Bedrock chat

Jamba Instruct v1.0 is an Amazon Bedrock chat model.It supports a 70,000-token context windowwith up to 4,096 output tokens.Input is priced at $0.500/M tokens and output at $0.700/M tokens. Route Jamba Instruct v1.0 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 Jamba Instruct v1.0 spend

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

~3K in / ~400 out · 5K req/day
3,000
070,000
400
04,096
5,000
01,000,000
Per request
$0.001780
in $0.001500 · out $0.000280
Per day
$8.90
5,000 requests
Per month
$271
152,188 requests

Estimate uses $0.5000/M input · $0.7000/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 $0.500/M
Output $0.700/M
Blended (3:1 in:out) $0.550/M
Cross-model comparable price

Context & limits

Context window
70,000 tokens
Max input
70,000 tokens
Max output
4,096 tokens
Modalities
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

Strengths & caveats — when to pick Jamba Instruct v1.0

Data-driven from Jamba Instruct v1.0's percentile within chat peers.

Where it's strong

Watch out for

  • !limited context — 70,000-token window is in the bottom quartile; not ideal for long documents or large RAG
  • !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

Benchmarks pending

We haven't logged public benchmark scores for Jamba Instruct v1.0 yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Jamba Instruct v1.0 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.
# Jamba Instruct v1.0 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="bedrock/ai21-jamba-instruct-v1-0",
    messages=[{"role": "user", "content": "Hello, Jamba Instruct v1.0!"}],
)

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="bedrock/ai21-jamba-instruct-v1-0",
    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 ↗

Explore similar models

Jamba Instruct v1.0 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 Jamba Instruct v1.0 cost?

Input is priced at $0.500 per 1M tokens and output at $0.700 per 1M tokens (Amazon Bedrock, last verified Oct 22, 2026).

What is the context window of Jamba Instruct v1.0?

Jamba Instruct v1.0 supports a 70,000-token context window with up to 4,096 output tokens.

Does Jamba Instruct v1.0 support function calling?

Jamba Instruct v1.0 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 Jamba Instruct v1.0 good for production?

Jamba Instruct v1.0 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 Jamba Instruct v1.0 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 Jamba Instruct v1.0

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