Together vs Fireworks vs jusInfer

All three host open-weights LLMs (Llama, Qwen, DeepSeek, Hermes, Mixtral, Mistral) behind OpenAI-compatible endpoints. They sound similar on the homepage. They serve different jobs. This post lays out the differences with specific numbers from May 2026 and tells you when to pick each.

What each actually does

Property	Together.ai	Fireworks	jusInfer
Primary job	Cheap open-weights hosting at scale	Fast serving via MoE / quantization	Per-call coding-optimized routing
You pick the model	Yes — explicit per call	Yes — explicit per call	No (use jusInfer-auto); or yes (use specific id)
Catalog size	~200 models	~80 models, curated	~25 curated for coding workloads
Tool use / JSON mode	Yes on most models	Yes on most models	Yes — normalized server-side
Routing across providers	Within Together only	Within Fireworks only	Across Together / Fireworks / Cloudflare / Anthropic / OpenAI
Cost optimization story	"Cheap per token"	"Fast → less wall time → cheap"	"Right model per call → cheap per task"
Per-user caps / seat billing	No	No	Yes (built-in)
Best fit	Power users with one preferred model	Latency-sensitive production	Coding agents on default settings

Per-token pricing (typical, USD/M tokens)

Prices change monthly. Verify on each site before committing. Snapshot as of May 2026:

Model	Together	Fireworks	jusInfer (auto-routed blended)
Llama 4 405B (Maverick)	$0.88 in / $0.88 out	~$0.90 / $0.90	n/a (routed only via specific ID)
Qwen3 Coder 480B	$0.90 / $1.20	$0.90 / $1.20	n/a (routed only via specific ID)
DeepSeek V4	n/a	$0.27 / $1.10	n/a (routed only via specific ID)
Llama 4 8B / Qwen3 8B	$0.18 / $0.18	$0.20 / $0.20	n/a (routed only via specific ID)
Typical coding-task blended (jusInfer-auto picks per call)	n/a — fixed per model	n/a — fixed per model	$0.20–$1.00 / 1M tokens

The point of the last row: jusInfer's value isn't per-token. It's that 80% of your agent's traffic doesn't need a 405B model. We route the tactical traffic to 8B-30B for pennies and reserve the 405B (or Anthropic/OpenAI frontier) for steps that actually need it. Average blended bill drops 60-80%.

Latency

Metric	Together	Fireworks	jusInfer
p50 first-token (popular 70B class)	350-500ms	200-300ms	passes through underlying provider
Long-context (32k+ input)	OK but degrades	Good — MoE handles long context well	depends on routed model
Tool-call throughput	Standard	High — function-calling-tuned variants	passes through

Fireworks generally wins on first-token. Together generally wins on catalog breadth. jusInfer adds zero latency vs. its underlying provider — we route, we don't proxy.

When to pick each

Pick Together if…

• You've benchmarked a specific open-weights model and want to run it always.
• You need a model that's only on Together's catalog (some niche or just-released open weights).
• You want the cheapest hosted rate for one model and can hand-tune your agent.
• You don't mind per-call model selection living in your agent code.

Pick Fireworks if…

• Latency matters more than catalog breadth.
• You're production-serving a chat or agent app where p95 first-token determines UX quality.
• Your workload concentrates on a handful of popular models (Llama 4, Qwen3, DeepSeek V4).
• You're okay with explicit per-call model selection.

Pick jusInfer if…

• You're running a coding agent and want the model selection done for you.
• Your bill is bigger than you'd like and you're tired of one-model-fits-all routing.
• You want per-user spend caps + seat billing built in.
• You want one bill across Anthropic + OpenAI + Together-style open weights rather than three vendor accounts.
• You'd rather have a routing tier handle model decisions than build that logic into your agent.

The router-vs-host distinction

Together and Fireworks are hosts — they own GPUs, serve models, set per-token prices. jusInfer is a router — we route requests to hosts (including Together and Fireworks, plus Anthropic, OpenAI, Cloudflare Workers AI), and we pick which host + which model to use per call.

That means:

• If you only ever want one model, a host is simpler — no abstraction layer.
• If you want the cheapest-capable answer per call, a router does the per-call decision for you.

Migration notes

• Together → jusInfer: change base_url from https://api.together.xyz/v1 to https://api.jusinfer.com/v1 and the key. To preserve your exact model choices, keep the explicit model id (e.g., meta-llama/Llama-4-405b-instruct). To switch to auto-routing, change the model to jusInfer-auto.
• Fireworks → jusInfer: same shape (https://api.fireworks.ai/inference/v1 → https://api.jusinfer.com/v1). Provider-prefixed ids (accounts/fireworks/models/...) are honored if you want to pin.
• Both → jusInfer: if you're using both and want to consolidate, jusInfer routes to either underneath transparently. One key, one bill.

What jusInfer doesn't do

Be honest about the boundaries:

• No fine-tune hosting. If you have a custom-trained Llama, host it on Together or Fireworks directly.
• No exotic model catalog. We route to ~25 coding-relevant models. If you need an embedding model, image-gen model, or research-only release, go upstream.
• No specialty workloads. Computer-use agents, RAG-only apps, image-gen agents — not our focus.

Setup checklist (jusInfer)

1. Sign up at jusinfer.com/login.
2. Mint a jinf_ key at /developer → Keys.
3. Change base_url in your client to https://api.jusinfer.com/v1.
4. Set model to jusInfer-auto (or keep an explicit provider/model id).
5. Run a normal workflow for a day. Check spend on the Usage tab.

Related reading

• OpenAI-compatible drop-in (every popular harness)
• The cheapest LLM API for coding agents in 2026
• OpenRouter alternatives in 2026
• Hermes models for coding agents

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Raw markdown: /blog/together-vs-fireworks-vs-jusinfer.md