Which routes belong in your shortlist?
| Requirement | Groq GPT OSS | OpenAI Luna / Sol |
|---|---|---|
| First cost trial | Compare 20B and 120B separately | Start with Luna |
| Harder reasoning | Evaluate 120B on your tasks | Evaluate Sol against the cheaper routes |
| Interactive response | Measure first-token and complete-response latency | Measure the same requests and concurrency |
| Cached prompts | Check the selected model’s cache terms | Account for hits, writes and retention |
| Existing integration | Test OpenAI-compatible request behavior | Check the exact model and endpoint |
| Llama access | Enterprise terms, not this self-serve comparison | Not a like-for-like hosted Llama route |
| Pricing data: How we verify → | ||
The chart, calculator and rate table above use maintained structured data rather than a second set of hardcoded prices. They compare token spend, not quality or a guaranteed latency advantage. See the Groq API pricing guide for the wider catalog.
The official Groq model directory marks both Llama 3.1 8B and Llama 3.3 70B Enterprise — Contact Sales. They are excluded from this article’s price widgets: historical public rates are not a current enterprise quote. Confirm availability, minimum commitments and limits with Groq before including either in a purchase decision.
Compare your workload, not the cheapest input column
For a reproducible monthly example, set the inline calculator above to 12 million total tokens: it assumes 9 million input and 3 million output tokens. For a different mix, use the full token calculator in Manual mode: enter 10 million uncached input tokens and 2 million output tokens, then repeat with your measured cache-hit share. Keep output volume and request mix equal across routes for the initial token-only comparison.
Next, replay a representative sample and count accepted results. Include the cost of rejected drafts, retries and escalation: a lower token bill can lose its advantage when more attempts are needed. Different tokenizers and response lengths mean equal-token charts are not equal-work benchmarks.
OpenAI’s published rate card separates standard and Batch pricing, cache hits, cache writes and long-context billing. Do not apply a cached-read price to all input or assume a short-context estimate covers a long document. Tool calls and external services need their own budget.
When to choose Groq or OpenAI
Test Groq first when you want hosted open-model inference and response latency is an important acceptance criterion. GPT OSS 20B is a useful initial routing or support-draft candidate; compare 120B if the smaller model misses your quality threshold. These are evaluation starting points, not benchmark-backed winners.
Test OpenAI first when your existing workflow depends on an OpenAI model, endpoint or tool. Use Luna as a cost control, then evaluate Sol only where a cheaper route fails. Check each required modality and tool against the exact model documentation; the provider name alone does not establish support.
For a managed open-model alternative, include Novita in the same workload trial. Compare the exact host, model, data-handling terms and rate rather than assuming matching model families produce identical service.
Affiliate disclosure: we may earn a commission from the sponsored link at no extra cost to you. It does not affect our recommendations.
Put routing behind acceptance checks
| Workload | Initial trial | Escalation trial |
|---|---|---|
| Intent classification | Groq GPT OSS 20B vs Luna | GPT OSS 120B |
| Support drafts | Groq GPT OSS 20B vs Luna | Sol when cheaper routes fail |
| Coding assistance | GPT OSS 120B vs Luna | Sol |
| Enterprise Llama workload | Obtain Groq terms first | Evaluate accessible alternatives |
The tracked qwen/qwen3-32b route is legacy and absent from the current Groq catalog. Do not treat a newer Qwen endpoint as the same model or transfer its rates and limits.
Set a request budget, cap output and retries, and log the actual model used. A fallback that silently switches to a premium route can change the bill more than a modest difference in base rates. Compare cost per accepted result and latency at your expected concurrency before increasing traffic.
Use Groq pricing and OpenAI pricing for full catalogs, and the token calculator to change volumes.
Sources and evidence boundaries
We checked Groq’s model directory and OpenAI’s pricing document for this refresh. The former supports the Enterprise labels and GPT OSS shortlist; the latter supports separate billing dimensions. Editorial source checks do not refresh Data-owned price-verification clocks. Read the verification labels on the generated tables; unavailable or stale evidence is not a newly verified rate.
FAQ
Is Groq cheaper than OpenAI?
Not universally. Compare GPT OSS 20B and 120B against Luna and Sol using your input, output, caching and retry mix.
Can I budget Llama 3.1 8B from an old Groq rate?
No. Groq lists it as Enterprise, so obtain current access, price and limit terms before budgeting.
Is Groq faster?
Measure both providers on the same tasks at your expected concurrency. First-token latency and total task completion time answer different questions.
Does an OpenAI-compatible API mean identical behavior?
No. Test required request fields, tool behavior, limits and response handling on the exact hosted model.