QwQ-32B reasoning API: provider comparison

Building with QwQ-32B means choosing where to run it. The landscape of QwQ-32B reasoning API providers has consolidated around a few GPU clouds and aggregators, each with distinct tradeoffs in price, latency, and API shape. This post compares the real options engineers hit when shipping reasoning workloads, from dedicated inference clouds to unified gateways.

The contenders

We’re comparing five ways to call the same open-weight model:

• Together AI — dedicated GPU cloud, first-class open-model hosting.
• Fireworks AI — inference optimizer with custom kernels and high throughput.
• DeepInfra — serverless open-model hosting, pay per token.
• OpenRouter — aggregator that routes to multiple backends behind one key.
• Replicate — containerized model runs, billed by compute time.

Gateways like n4n.ai sit adjacent to this list: one OpenAI-compatible endpoint that addresses 240+ models with automatic fallback when a provider is rate-limited, which is useful if you want QwQ-32B plus others without writing routing logic.

Capabilities

All five serve the reference QwQ-32B weights (or a near-identical reproduction). The model is a 32B-parameter reasoning specialist: it emits long chain-of-thought before a final answer, and it does not natively support tool calling or structured JSON the way Qwen2.5-Instruct does.

Where they diverge:

• Context window: Most expose the native 32K token context. Fireworks and Together have experimentally extended some deployments to 128K via RoPE scaling, but expect higher memory cost.
• Sampling controls: Together and DeepInfra give raw logprobs and top_logprobs. Fireworks exposes n completions and repetition penalties. Replicate’s Cog container often lags on new sampling flags.
• Reasoning visibility: QwQ’s think tokens are returned inline in the message content. None of the providers currently strip or summarize the CoT for you—you parse it client-side.

from openai import OpenAI

client = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER_KEY")
stream = client.chat.completions.create(
    model="Qwen/QwQ-32B",
    messages=[{"role": "user", "content": "Prove sqrt(2) is irrational."}],
    stream=True,
)
for chunk in stream:
    print(chunk.choices[0].delta.content or "", end="")

Price and cost model

None of these providers publishes a fixed QwQ-32B price that survives a quarter, but the models are stable:

• Together / Fireworks: Per-token billing, input and output priced separately. Output is typically 2–3× input cost because generation is compute-bound.
• DeepInfra: Aggressively per-token, often cheaper than dedicated clouds, but you trade guaranteed capacity.
• OpenRouter: Markup over the underlying provider (usually 5–10%). You can pin a specific backend or let it pick the cheapest available.
• Replicate: Billed per second of GPU time, not per token. For a 32B model generating 2K reasoning tokens, this is almost always more expensive than per-token APIs.

If you meter internally, a gateway with per-token usage metering (e.g., n4n.ai’s forwarding) keeps your cost attribution clean when you fan out across backends.

Latency and throughput

QwQ-32B is slow per token because it writes a lot of intermediate reasoning. Time-to-first-token (TTFT) is dominated by prompt processing; inter-token latency is dominated by decode bandwidth.

• Fireworks wins on TTFT thanks to fused attention kernels. Expect ~2–3× lower TTFT than vanilla vLLM on the same hardware.
• Together runs heavily sharded A100/H100 pools; steady-state throughput is good, cold starts rare.
• DeepInfra serverless can cold-start a 32B container in 5–20s if idle—painful for interactive use.
• OpenRouter latency is whatever the routed backend gives; if it falls back to a congested provider, you feel it.
• Replicate is worst: container boot plus model load can add 30s+ to the first call.

For streaming, all support SSE. None yet support bidirectional streaming for partial CoT correction.

Ergonomics

Every provider here mirrors the OpenAI Chat Completions schema, so your existing SDK works with a base_url swap. Differences:

• Together and Fireworks ship first-party Python/TS SDKs with typed model enums.
• DeepInfra relies on the generic OpenAI client; model IDs are case-sensitive and verbose (deepinfra/QwQ-32B).
• OpenRouter adds routing headers (HTTP-X-Router-Force-Provider) and honors provider cache-control hints if you send them.
• Replicate breaks the pattern: you call replicate.run() with a Cog model version hash, not a chat endpoint.

curl https://api.fireworks.ai/inference/v1/chat/completions \
  -H "Authorization: Bearer $FW_KEY" \
  -d '{"model":"accounts/fireworks/models/qwq-32b","messages":[{"role":"user","content":"9.11 or 9.9?"}]}'

Ecosystem

• Together: Fine-tuning, batch inference, and a model marketplace. Good if QwQ is one of many models you train.
• Fireworks: LLM eval suite and function-calling beta for other models; QwQ is treated as a raw reasoning endpoint.
• DeepInfra: Largest catalog of open weights per dollar; minimal surrounding tooling.
• OpenRouter: Single key for 200+ models, community rankings, and client-side model switching.
• Replicate: Huge community of Cog models; easy to fork the QwQ container and add preprocessors.

Limits

Concrete ceilings engineers hit:

• Context: 32K standard; extended contexts are provider-specific and may silently truncate.
• Concurrency: Together and Fireworks sell dedicated endpoints for >50 concurrent streams. DeepInfra serverless throttles at low default RPM.
• Rate limits: OpenRouter free tier is tight; Replicate scales with your billing tier but per-second billing caps runaway spend only after the fact.
• Output caps: Most cap a single completion at 8K–16K tokens, which can clip long QwQ reasoning traces.

Comparison table

Which to choose

Latency-sensitive production (user-facing reasoning) Use Fireworks or Together. Fireworks if you need the lowest TTFT and can live with their extended-context beta; Together if you want predictable dedicated capacity and fine-tuning later.

Cost-driven batch jobs (offline proof generation) DeepInfra per-token pricing beats per-second Replicate by an order of magnitude. Accept cold starts by warming via a preflight call, or schedule batches during off-peak.

Multi-model routing without writing a router OpenRouter or a gateway like n4n.ai. You get one key, fallback when a provider is degraded, and can pin QwQ-32B or let cost direct the route. Useful when QwQ is part of a larger model mix.

Experimentation and custom preprocessing Replicate wins only if you need to fork the inference container—say, to inject a custom tokenizer or run vision adapters alongside. Otherwise avoid the per-second tax.

Single-vendor simplicity If your stack already uses Together for embeddings and fine-tunes, don’t add a second provider for QwQ. The model is identical; the differentiator is your operational surface area.

QwQ-32B reasoning API providers are converging on OpenAI-compatible shapes, so the switch cost is low. Pick by where the model runs when your p99 latency budget or your AWS bill complains.