Thenlper Gte Base
Fireworks AI embeddingThenlper Gte Base is a Fireworks AI embedding model.It supports a 512-token context window.Input is priced at $0.008000/M tokens Route Thenlper Gte Base via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.
Estimate Thenlper Gte Base spend
Pick a workload, fine-tune the sliders, and see the monthly bill.
Estimate uses $0.008000/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 May 12, 2026.
| Input | $0.008000/M | |
| Output | — |
Limits
- Context window
- 512 tokens
- Max input
- 512 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
- !high cost — input + output rates are in the top 93% of priced embedding peers; consider a cheaper sibling for high-volume workloads
- !limited context — 512-token window is in the bottom quartile; not ideal for long documents or large RAG
- !small context (under 16K tokens)
Benchmarks pending
We haven't logged public benchmark scores for Thenlper Gte Base yet. Have one to contribute? Submit a source — citations help us prioritise.
Call Thenlper Gte Base 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.
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.# Thenlper Gte Base 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="fireworks-ai/thenlper-gte-base",
messages=[{"role": "user", "content": "Hello, Thenlper Gte Base!"}],
)
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="fireworks-ai/thenlper-gte-base",
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"))AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗Compare with similar models
Thenlper Gte Base doesn't have a public Arena ELO score yet, so we group by provider only — quality-tier comparisons need a benchmark.
- Accounts Fireworks ModelsFireworks AI · $0.1000/M in · $0.000000/M out · 40,960 ctx
- Nomic AI Nomic Embed Text v1Fireworks AI · $0.008000/M in · $0.000000/M out · 8,192 ctx
- Nomic AI Nomic Embed Text v1.5Fireworks AI · $0.008000/M in · $0.000000/M out · 8,192 ctx
- Thenlper Gte LargeFireworks AI · $0.0160/M in · $0.000000/M out · 512 ctx
FAQ
How much does Thenlper Gte Base cost?
Input is priced at $0.008000 per 1M tokens and output at $0.000000 per 1M tokens (Fireworks AI, last verified May 12, 2026).
What is the context window of Thenlper Gte Base?
Thenlper Gte Base supports a 512-token context window.
Does Thenlper Gte Base support function calling?
Thenlper Gte Base 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 Thenlper Gte Base good for production?
Thenlper Gte Base 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 Thenlper Gte Base 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 Thenlper Gte Base
Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.
Third-party evals — verify the marketing.
Cross-check our number against the rest of the ecosystem.