Together Tev1 4B Experimental

Together AI chat

Together Tev1 4B Experimental is a Together AI chat model.It supports a 32,768-token context window.Input is priced at $0.0420/M tokens Route Together Tev1 4B Experimental 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 Together Tev1 4B Experimental spend

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

~3K in / ~400 out · 5K req/day
3,000
032,768
400
016,000
5,000
01,000,000
Per request
$0.000126
in $0.000126 · out $0.000000
Per day
$0.6300
5,000 requests
Per month
$19.18
152,188 requests

Estimate uses $0.0420/M input · $0.000000/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 $0.0420/M
Output —

Context & limits

Context window
32,768 tokens
Max input
32,768 tokens
Max output
—
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 Together Tev1 4B Experimental

Data-driven from Together Tev1 4B Experimental's percentile within chat peers.

Where it's strong

Watch out for

  • !high cost — input + output rates are in the top 100% of priced chat peers; consider a cheaper sibling for high-volume workloads
  • !limited context — 32,768-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 Together Tev1 4B Experimental yet. Have one to contribute? Submit a source — citations help us prioritise.

Try it

Call Together Tev1 4B Experimental 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.
# Together Tev1 4B Experimental 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="together-ai/together-tev1-4b-experimental",
    messages=[{"role": "user", "content": "Hello, Together Tev1 4B Experimental!"}],
)

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="together-ai/together-tev1-4b-experimental",
    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

Together Tev1 4B Experimental 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 Together Tev1 4B Experimental cost?

Input is priced at $0.0420 per 1M tokens and output at $0.000000 per 1M tokens (Together AI, last verified Oct 22, 2026).

What is the context window of Together Tev1 4B Experimental?

Together Tev1 4B Experimental supports a 32,768-token context window.

Does Together Tev1 4B Experimental support function calling?

Together Tev1 4B Experimental 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 Together Tev1 4B Experimental good for production?

Together Tev1 4B Experimental 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 Together Tev1 4B Experimental 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 Together Tev1 4B Experimental

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