Open-Source LLMs vs OpenAI & Anthropic (2026)

Someone has told you that open models served through an API are 5–20x cheaper than GPT or Claude, and now every AI feature on your roadmap looks overpriced. Sometimes that's true. In 2026 it's just as often false, because the frontier labs have priced their small models to match — the real savings come from routing each task to the right model.

Should a SaaS use open-source LLMs or OpenAI and Anthropic?

Start on a frontier API from OpenAI, Anthropic or Google behind a thin abstraction, then move high-volume, simple tasks to open-weight models on a hosted inference API once your evals show they hold up. Self-host open weights only at very high volume, under strict data-residency terms, or when you need deep fine-tuning.

The "5–20x cheaper" claim holds against frontier mid-tier and flagship models. It fails against the frontier labs' small models, which now sit at the same price point. So the question isn't open versus closed; it's which model each task deserves. Which frontier vendor to start with is a separate decision, covered in our OpenAI vs Anthropic comparison.

What are your three options?

Hosted open weights — Llama, Qwen, DeepSeek, Mistral, OpenAI's gpt-oss — are downloadable models served by a company that runs the GPUs and bills per token: what most founders mean by "open source".

Are open-weight model APIs really 5–20x cheaper?

Standard prices as of October 2026, in US dollars per million tokens. Vendors change pricing often, so check the live pages — Anthropic's and DeepInfra's among them — before you budget.

These rates apply to normal-length prompts; OpenAI charges more above 272K input tokens and Claude Haiku 5.5 above 100K.

Against mid-tier and flagship models, the claim is true. gpt-oss-120b is about 13x cheaper on input and 17x on output than Claude Sonnet 5.5 or GPT-6.1 Sol; DeepSeek V4 Flash is about 22x and 56x cheaper.

Against frontier small models, it's false. Claude Haiku 5.5 and GPT-6 Luna undercut gpt-oss-120b on both input and output, and come within a cent of DeepSeek V4 Flash on input.

Big open models aren't always cheap. Kimi K3 costs more per token than Claude Sonnet 5.5. "Open" describes a licence, not a price tier.

The host matters as much as the model. Llama 3.3 70B is about 10x cheaper on input at DeepInfra than at Together AI. A cheaper listing can mean a different precision or a busier tier, so run evals against the exact endpoint you'll pay for.

What does a typical AI feature cost per month?

Take a support-ticket feature that classifies, summarises and suggests a reply, running 100,000 times a month. Each call sends 2,000 input tokens (system prompt, ticket, some retrieved context) and gets 300 back: 200 million input and 30 million output tokens a month.

Moving from Sonnet 5.5 to gpt-oss-120b saves about $650 a month — a 14.6x cut, squarely inside "5–20x". Moving from Haiku 5.5 to gpt-oss-120b makes it more expensive. Moving from Haiku 5.5 to DeepSeek V4 Flash saves $11.60 a month, which won't pay for the extra vendor, data processing agreement and eval suite.

So the 5–20x saving is real mainly when a mid-tier model is doing a small model's job — and the first fix for that is dropping a tier with the vendor you already use. Discounts shift both sides further: OpenAI and Anthropic take 50% off batch requests, and cached input on Haiku 5.5 and GPT-6 Luna costs a tenth of the normal rate. Tokens are rarely the biggest cost of an AI feature anyway — see what it costs to build an AI app.

How do open models compare on quality, speed and features?

Quality. Leaderboards are a shortlist, not a verdict. As a rule of thumb, strong open-weight models handle classification, extraction and short summaries well, while frontier models earn their price on multi-step reasoning, agentic tool chains and writing customers read. Your own evals decide.

Speed. Groq lists gpt-oss-120b at around 500 tokens per second and gpt-oss-20b at around 1,000. For streaming chat, that can matter more than price.

Context length. Claude's 5.5 models offer 1M-token windows and OpenAI's GPT-6 models 1.05M. Hosted open models are usually shorter: 131,072 tokens for gpt-oss-120b on Groq, 128K for Llama 3.3 70B. DeepSeek V4 Flash is the exception at 1M.

Tool calling and structured output. OpenAI and Anthropic both constrain output to your JSON schema. Open hosts vary: Groq supports tool use on every model it hosts but guarantees schema-matching output only on the gpt-oss models and two others, and Together AI supports JSON-schema responses without promising constrained decoding.

What about data privacy and UK GDPR?

Every option except self-hosting makes the provider a processor of whatever your users type, and the defaults differ:

• OpenAI and Anthropic don't train on API data by default, keep it for up to 30 days, and offer zero data retention to approved customers. OpenAI's UK data-residency region covers storage, not processing.
• Fireworks stores no prompts or outputs for open models by default; its Responses API is the exception unless you set store to false.
• Groq doesn't retain inference data by default but may log it for up to 30 days unless you enable zero data retention, which any customer can. Stored data sits in the US.
• Together AI stores prompts and responses unless an admin enables zero data retention, and doesn't train on them without consent.
• DeepInfra says inference data isn't written to disk or used for training.

Most of these process in the US, so a UK SaaS sending them personal data needs a data processing agreement, a valid international transfer mechanism and a privacy notice that names them — see our UK GDPR guide for SaaS startups. This is general guidance, not legal advice.

Do open-weight licences matter for a SaaS?

Less through an API, more when you fine-tune, self-host or redistribute. gpt-oss-120b and Qwen3 235B use Apache 2.0, and DeepSeek V4 Flash uses MIT — all permissive. Llama 3.3's Community License is not: companies above 700 million monthly active users need a separate licence from Meta, products that include it must display "Built with Llama", and fine-tuned models you distribute must have names beginning with "Llama". Read the licence of the exact model you ship.

How do you mix cheap and frontier models safely?

With a router: each task goes to the cheapest model that passes its evals, with a frontier model behind it as the fallback.

const routes = {
  classify: { provider: "groq", model: "openai/gpt-oss-120b" },
  summarise: { provider: "anthropic", model: "claude-haiku-5-5" },
  draftReply: { provider: "anthropic", model: "claude-sonnet-5-5" },
} as const;

export async function runTask(task: keyof typeof routes, input: string) {
  try {
    return await callModel(routes[task], input); // throws if schema check fails
  } catch {
    return await callModel(FRONTIER_FALLBACK, input);
  }
}

Three habits make it work:

• One thin provider layer. Every model call goes through one module, prompts live in your repository, and the rest of the app asks for a task, not a vendor.
• Evals before every switch. Score any candidate model against the current one on a few hundred real, anonymised examples per task, then watch the fallback rate once it's live.
• Caching. Cache identical requests yourself, and keep long system prompts stable so provider prompt caching applies.

When does self-hosting open weights make sense?

OpenAI's gpt-oss-120b fits on a single 80GB GPU such as an NVIDIA H100, which rents for $2.20 an hour on DeepInfra and $8.00 on demand at Fireworks. Across a 730-hour month that's $1,606 to $5,840 for one GPU — before a second for redundancy, monitoring and the engineering time to run it. Our example feature cost $48 a month on hosted gpt-oss-120b; you'd need about 33 times that volume before even the cheaper GPU breaks even on paper.

Self-hosting earns its keep with very high, steady volume, contracts that keep data inside your own environment, or customisation beyond hosted fine-tuning. For an MVP, none of those usually applies.

Our default: frontier API first, open models where evals allow

If you're asking what tech stack is best for an AI SaaS MVP, the model is one layer of it. For the AI-powered SaaS products we build, we start on a frontier API behind a thin provider layer — the same call as in our SaaS MVP tech stack, server-side in the same Next.js and Postgres app as everything else. We find the quality bar on a mid-tier model, then push each task down to the cheapest model that passes its evals: first the vendor's small tier, then an open-weight model on a hosted API once volume justifies a second vendor.

We deviate for concrete reasons: a contract requiring a specific processing region, a latency-critical feature suited to a fast host like Groq, or a high-volume task an open model clears from day one. We don't self-host for an MVP, or pick a model before deciding whether the feature needs AI at all.

Frequently asked questions

Are open-source LLMs good enough for a production SaaS?

Yes, for many high-volume tasks — classification, extraction, tagging and short summaries — provided each feature passes your own evals first. Frontier models still earn their price on multi-step reasoning, agentic workflows and long-form writing. Test feature by feature, not model by model.

Is it cheaper to self-host an open model than to use an API?

Rarely at MVP scale — one rented H100 running all month costs more than our example feature spends on tokens, even on Claude Opus 5.5. At $2.20 an hour, one GPU is about $1,606 a month before redundancy and engineering time. Self-hosting pays off only with high, steady volume or strict data-residency needs.

Do hosted open-model providers keep or train on your data?

Defaults differ by provider, so check each one's settings. Fireworks keeps nothing for open models by default and DeepInfra doesn't write inference data to disk. Groq may log for up to 30 days unless you enable zero data retention, and Together AI stores prompts unless an admin turns that off.

Can you move from OpenAI or Anthropic to an open model later?

Yes — if every call goes through one provider layer, moving a task is a config change plus an eval run. Groq, Together AI, Fireworks and DeepInfra all offer OpenAI-compatible endpoints, so the plumbing is the easy part. The real work is retuning prompts and confirming structured output still parses.

Want AI features with margins that survive growth?

The right model mix depends on your features, volumes and buyers' contracts. If you're a UK founder building an AI-native SaaS and want each feature matched to a model and its running cost modelled, book a free scoping call. We'll plan it with you and quote the build fixed-price, so you know the cost before you commit.