Fireworks AI Llama 4 Scout benchmarks vs Together AI

The Fireworks AI Llama 4 Scout benchmark vs Together AI question comes up the moment you need a fast, cheap open-weight model in production. Both platforms host Meta’s Llama 4 Scout, but they differ sharply in how they expose the model, what they charge, and how they behave under load.

Capabilities and model access

Weight packaging

Fireworks AI publishes Llama 4 Scout under a namespaced model ID and often ships a quantized build optimized for its inference stack. Together AI mirrors the reference Hugging Face weights, which makes it easier to swap in community fine-tunes without changing the model string.

Function calling and structured output

Both providers implement the OpenAI function-calling schema. In practice, Fireworks enforces JSON schema via a stricter response_format extension, while Together relies on the standard json_object mode and leaves constraint validation to the client. For a model the size of Scout, either is sufficient for lightweight tool routing.

Precision and context

Llama 4 Scout ships with a fixed context window; neither provider extends it via rope scaling by default. Fireworks may serve an FP8 variant; Together typically serves BF16 unless you request a quantized endpoint. The difference is invisible at the API level but shows up in memory footprint and marginal latency.

Price and cost model

Both platforms meter per token. Input tokens are cheaper than output tokens on either side. Fireworks applies a small premium on its low-latency route; Together offsets per-token cost with an optional monthly membership that lowers the effective rate as volume climbs.

Cached prompt tokens are discounted on both. Fireworks honors explicit cache-control hints; Together applies automatic prefix caching. The code below shows how to send a cache directive on Fireworks.

from openai import OpenAI
client = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key="fk-...")
client.chat.completions.create(
    model="accounts/fireworks/models/llama-4-scout",
    messages=[{"role": "system", "content": "You are a terse helper."}],
    extra_headers={"cache-control": "ephemeral"},  # provider hint
)

Together’s caching is implicit; you just repeat the prefix and get a lower billed amount.

Latency and throughput

The Fireworks AI Llama 4 Scout benchmark vs Together AI split is most visible here. Fireworks tunes for time-to-first-token (TTFT) on single-stream requests using fused kernels. Together tunes for aggregate throughput when many requests batch on the same GPU pool.

If you serve a chatbot with one active user per connection, Fireworks gives lower p50 TTFT. If you run a nightly job that fans out 500 concurrent summarization calls, Together sustains higher tokens/sec per dollar.

Below is a minimal loop to record TTFT against either endpoint. It does not invent numbers; run it against your account.

import time, openai
def ttft(base_url, key, model):
    c = openai.OpenAI(base_url=base_url, api_key=key)
    start = time.perf_counter()
    stream = c.chat.completions.create(
        model=model, messages=[{"role":"user","content":"Write a haiku."}],
        stream=True)
    for chunk in stream:
        if chunk.choices[0].delta.content:
            return time.perf_counter() - start
print(ttft("https://api.together.xyz/v1", t_key, "meta-llama/Llama-4-Scout"))

Reproducing the benchmark responsibly

Do not trust a single run. Warm the endpoint with five discarded requests, then collect p50/p95 over 100 iterations. Network jitter between your runner and the provider region will dominate if you benchmark from a laptop. Run from the same cloud region where your production service lives.

Ergonomics and API design

Both are OpenAI-compatible. You can swap base_url and api_key on the same SDK. Fireworks adds request_timeout and returns a trace_id header for support tickets. Together’s client retries on 429 with exponential backoff out of the box.

A gateway such as n4n.ai can sit in front of both, presenting one OpenAI-compatible endpoint that addresses 240+ models and automatically falls back when a provider is rate-limited, which removes the need to code dual clients.

# Same code, different base
from openai import OpenAI
fw = OpenAI(base_url="https://api.fireworks.ai/inference/v1", api_key=fw_key)
tg = OpenAI(base_url="https://api.together.xyz/v1", api_key=tg_key)

Streaming works identically. One nuance: Fireworks emits usage only at stream end if you pass stream_options={"include_usage": True}; Together does the same but defaults to omitting it.

Ecosystem and tooling

Fireworks provides a hosted playground and version-pinned model registry, so you can lock llama-4-scout@v1.2. Together ships a CLI that submits async batch jobs and a model zoo with community adapters. For fine-tuning, Fireworks exposes a job API with uploaded dataset URLs; Together offers an in-place fine-tune that reuses the same model ID suffix.

Observability is comparable: both return request IDs in headers. Fireworks adds a x-fireworks-trace header; Together uses x-request-id.

Limits and quotas

Fireworks enforces token-per-minute budgets that scale with tier; exceed them and you get a 429 with Retry-After. Together caps concurrent requests per model, which is better for batch but can surprise interactive apps. Neither allows unlimited burst without sales contact.

Read the headers:

curl -i -H "Authorization: Bearer $FW_KEY" \
  -d '{"model":"accounts/fireworks/models/llama-4-scout","messages":[{"role":"user","content":"hi"}]}' \
  https://api.fireworks.ai/inference/v1/chat/completions | grep -i "retry-after\|x-ratelimit"

Operational caveats

Neither provider guarantees zero regressions across model version bumps. Pin the model version on Fireworks to avoid silent upgrades. On Together, suffix the model ID with a date stamp if available. Monitor the x-model-version header where provided.

Both count system prompts as input tokens; if you embed long instructions, cache them explicitly to cut cost. Assume both operate primarily in US regions unless their status page says otherwise—verify before shipping latency-critical EU workloads.

Head-to-head comparison

Which to choose

Interactive, latency-bound services

Pick Fireworks AI. The fused kernels and strict JSON mode keep p95 TTFT low. If you want resilience without writing fallback logic, route through a gateway that honors client routing directives.

Bulk extraction or offline scoring

Together AI wins. Its concurrency caps align with batch frames, and membership pricing reduces effective cost when you push millions of output tokens nightly.

Multi-provider experimentation

Keep both clients behind a thin wrapper, or use one OpenAI-compatible endpoint that forwards to either. You avoid vendor lock and can A/B the Fireworks AI Llama 4 Scout benchmark vs Together AI on your own traffic.

Strict budget with predictable volume

Together’s membership flattens cost. Fireworks stays competitive for spiky, low-QPS workloads where you never hit the concurrent cap.

The Fireworks AI Llama 4 Scout benchmark vs Together AI verdict is workload-driven: measure your own p95 latency and effective $/M output tokens before committing. Both are competent OpenAI-compatible hosts; the right call is about shape of load, not raw model quality.