MemetikEdition 2026-09

Lists / AI infra

Best LLM APIs for AI startups (2026): What ChatGPT, Claude & Gemini Recommend

OpenAI is named in all 50 AI answers and first in 33. See where Anthropic, Google, DeepSeek, OpenRouter and the rest rank, and where the models disagree.

OpenAI is the LLM API the models name first. Ten AI models from the OpenAI, Anthropic, Google and Perplexity families answered five developer questions. OpenAI appeared in all 50 answers and came first in 33. Anthropic came first in 14 and Google in 2. The 15 APIs the models named follow in measured order, each with who should pick it and why. The ranking counts which APIs AI answers name. It doesn’t test which API performs best.

TL;DR

1. OpenAI

Pick OpenAI if you want one first-party API to start on and the name the models reach for first.

Measured: named 50 of 50 (OpenAI 100%), first 33 of 50 (OpenAI 66%), average position 1.52.

OpenAI’s lead sits in the first slot. No other API was put first more than 14 times, and all ten models named OpenAI in every answer. The answers treat it as the safe place to start and Anthropic as the quality challenger. A Sonar Pro answer put that split plainly:

“OpenAI is usually the safest pick for ecosystem depth and tooling, while Anthropic is often preferred for top-tier model quality and agentic reliability.”

DataCamp lists frontier reasoning models, real-time voice and agent-based development through the OpenAI Agents SDK on the OpenAI platform. It also warns that OpenAI can become expensive at scale, especially for high-volume or reasoning-heavy applications.

Pros

Cons

Pricing: per million input and output tokens, with a Batch API discount for non-real-time work, in MongoEngine’s dated figures.

Best for: general-purpose AI apps, reasoning, coding, multimodal workflows and production-ready AI products.

2. Google

Pick the Gemini API from Google if you want the second provider most answers pair with a default, or your product already lives in Google’s ecosystem.

Measured: named 50 of 50 (Google 100%), first 2 of 50 (Google 4%), average position 2.88.

Google has the widest gap in the panel between being named and being put first, at 96 points. The answers use Gemini as the second name beside a default. They cast it as the price, long-context or multimodal option. GPT-5.6 Luna’s answer on cost and quality routed cheaper workloads to Gemini or DeepSeek. Google’s own two models named it in every answer, but so did every other model, so there’s no home-team lean to discount.

DataCamp calls Gemini a good choice for coding tools and for products that connect with Google’s broader AI ecosystem.

Pros

Cons

Pricing: per million input tokens, with MongoEngine quoting the lighter Flash variant.

Best for: multimodal AI apps, Google ecosystem integration, long-context workflows and coding.

3. Anthropic

Pick Anthropic’s Claude API if coding, agents or long documents sit at the centre of your product.

Measured: named 46 of 50 (Anthropic 92%), first 14 of 50 (Anthropic 28%), average position 1.98.

Anthropic is the one API besides OpenAI that the models put first with any frequency. Its misses all come from two OpenAI models: GPT-5.6 Sol named it in 2 of 5 answers and GPT-5.6 Terra in 4 of 5. Every other model named it every time, the Claude models included (Anthropic 100% of their 15 answers). Claude Opus 5 flagged its own stake before it answered:

“I’m made by Anthropic, which sells one of the APIs you’d be choosing between (Claude). Take my read accordingly”

DataCamp positions Claude for careful instruction-following, strong writing quality, document analysis and complex prompts.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: coding assistants, enterprise AI, long-context analysis, document workflows and AI agents.

4. DeepSeek

Pick DeepSeek if cost is the binding constraint and you plan to route routine traffic away from a frontier default.

Measured: named 33 of 50 (DeepSeek 66%), first 1 of 50 (DeepSeek 2%), average position 4.45.

DeepSeek is the name the models reach for when budget comes up. A Sonar Pro answer to the startup prompt called it the usual cost-quality balance for a product that must stay cheap. Sonar Reasoning Pro went further and suggested a cheap DeepSeek or Gemini Flash model as the default, with OpenAI or Anthropic kept for the calls that need frontier quality. Its count comes from the Google, Anthropic and Perplexity models. GPT-5.6 Sol and GPT-5.6 Terra never named it.

DataCamp lists DeepSeek among the popular open models that open-source API providers serve.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: cost-sensitive products that send routine work to a lower-cost model, as the recorded answers frame it.

5. OpenRouter

Pick OpenRouter if you want many models behind one OpenAI-compatible endpoint while you’re still choosing between them.

Measured: named 27 of 50 (OpenRouter 54%), first 0 of 50 (OpenRouter 0%), average position 7.44.

OpenRouter is the router the models name most, and its count depends heavily on who you ask. The Gemini models named it in all ten of their answers (OpenRouter 100%). The three OpenAI models never did. Its own site, openrouter.ai, drew 15 citations, second among vendor-owned hosts. It arrives late when it is named, which fits a tool the answers treat as plumbing beside a default rather than the default itself.

Braintrust describes a managed API for reaching hundreds of models through a single endpoint, paid for with prepaid credits.

DataCamp adds that it suits developers who want to avoid being locked into one model vendor.

Pros

Cons

Pricing: pay-as-you-go with prepaid credits, with model pricing passed through at provider rates.

Best for: multi-model apps, model comparison, fallback routing and fast experimentation.

6. Amazon Bedrock

Pick Amazon Bedrock if your product already runs on AWS and you want several model families under AWS controls.

Measured: named 26 of 50 (Amazon Bedrock 52%), first 0 of 50 (Amazon Bedrock 0%), average position 6.88.

Bedrock is the multi-model route the OpenAI models take most seriously. They named it often (Amazon Bedrock 53.3% of their 15 answers), while their only router mention was a single LiteLLM. Across the whole panel its spread is even, with every model naming it between one and four times. Sonar Reasoning Pro put it on its default shortlist beside Azure OpenAI for teams deep in those clouds. A Claude Fable 5 answer placed the same pair in regulated enterprise environments.

DataCamp says Bedrock supports models from Amazon, Anthropic, Meta, Mistral AI, Cohere, DeepSeek and others. It calls Bedrock a strong choice for companies already building on AWS.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: AWS users, enterprise AI and security-focused deployments.

7. Groq

Pick Groq if response time is the constraint and your product can run on the open models Groq hosts.

Measured: named 25 of 50 (Groq 50%), first 0 of 50 (Groq 0%), average position 6.04.

Groq’s count rests on the Google and Anthropic models, with the Perplexity models behind them (Groq 60% of their 10 answers). The OpenAI models never named it. When it does appear, it comes earlier than most APIs named a similar number of times. Its average position of 6.04 sits ahead of OpenRouter and Amazon Bedrock, though both are named more often. That pattern fits a specialist pick for one job rather than a general default.

Braintrust says Groq is well-suited to real-time chat, voice, agent and streaming workloads where response time is the primary constraint. It adds that teams needing GPT, Claude or Gemini require an additional gateway alongside Groq.

Pros

Cons

Pricing: free tier with rate limits, then model-based pricing by input and output tokens.

Best for: latency-sensitive applications that use supported open models through an OpenAI-compatible API.

8. Azure OpenAI

Pick Azure OpenAI if your company already runs on Azure and a regulated setting shapes the choice.

Measured: named 24 of 50 (Azure OpenAI 48%), first 0 of 50 (Azure OpenAI 0%), average position 7.17.

Azure OpenAI is what the models name for an enterprise question. Sonar Reasoning Pro and Claude Fable 5 both paired it with AWS Bedrock, as the pick for a company already deep in one cloud or working in a regulated setting. Claude Sonnet 5 named it most, in 4 of 5 answers. The OpenAI models named it in 2 of 5 answers at most. Microsoft’s own documentation doesn’t appear among the five vendor-owned hosts the answers cited most, so the case for it here rests on the answers alone.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: teams already on Azure with regulated-enterprise requirements, as the recorded answers frame it.

9. Mistral

Pick Mistral if European hosting, open weights or deployment control drive the decision.

Measured: named 22 of 50 (Mistral 44%), first 0 of 50 (Mistral 0%), average position 6.14.

Mistral comes up when hosting location matters. GPT-5.6 Luna named it in 4 of 5 answers, for European hosting, open-weight models or cost control. GPT-5.6 Sol’s comparison answer set out when to add it, along with two names further down the list:

“Add Mistral when deployment control or European hosting matters, Cohere for enterprise retrieval, and xAI when its particular model behavior or ecosystem is strategically useful.”

DataCamp lists Mistral among the popular open models that open-source API providers serve.

Braintrust’s own gateway lists Mistral among the providers it calls, beside OpenAI, Anthropic, Google and AWS.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: European hosting, open-weight models or deployment control, as the recorded answers frame it.

10. Together

Pick Together if you’ve standardised on open models and want hosted inference and fine-tuning without running GPUs.

Measured: named 19 of 50 (Together 38%), first 0 of 50 (Together 0%), average position 7.16.

Together splits the panel sharply. Four of the ten models never mentioned it, while Claude Sonnet 5 and Gemini 3.6 Flash named it every time. A shortlist built by an AI assistant may or may not include it, depending on which assistant built it. For a team already committed to open models, it’s a host worth testing.

Braintrust describes serverless inference, batch inference, fine-tuning and dedicated endpoints for open and custom models. It says teams that need one gateway for OpenAI, Anthropic and Google usually pair Together AI with an aggregator.

DataCamp notes that model quality, speed and reliability can vary with the model selected.

Pros

Cons

Pricing: prepaid and credit-based, then billed per million tokens by model.

Best for: open-source model inference, fine-tuning and experimentation across many models.

11. Meta Llama

Pick Meta Llama if your token volume is high and predictable enough to justify an open model, run by a host or by you.

Measured: named 17 of 50 (Meta Llama 34%), first 0 of 50 (Meta Llama 0%), average position 6.29.

Meta Llama has one of the most uneven splits in the table. Gemini 3.6 Flash and Sonar Reasoning Pro named it in every answer. Six of the ten models named it once or not at all. The panel also counts a passing mention of “Meta” toward it, so the count is a ceiling on deliberate picks rather than a floor.

MongoEngine sets out the trade for open weights. Self-hosting brings zero per-token cost. It pays off when token volume is high and predictable, and usually not for early-stage products with uncertain demand. The middle ground is Llama through managed providers such as Together AI, Fireworks AI or Amazon Bedrock.

Pros

Cons

Pricing: zero per-token cost when self-hosted.

Best for: startups processing high volumes of text with predictable patterns.

12. LiteLLM

Pick LiteLLM if you want an open-source gateway you host yourself and have the infrastructure team to run it.

Measured: named 17 of 50 (LiteLLM 34%), first 0 of 50 (LiteLLM 0%), average position 8.59.

LiteLLM ties Meta Llama on mentions but arrives last. Its average position of 8.59 is the highest in the table, so it tends to close a list of options rather than open one. The Claude models and Gemini 3.6 Flash carry its count. It’s also the only router any OpenAI model named, once, in a GPT-5.6 Luna answer.

Braintrust describes LiteLLM as an open-source AI gateway and proxy that teams can use as a Python SDK or deploy as a central gateway for routing, spend tracking, virtual keys and budgets. It says LiteLLM fits teams that can manage the proxy, database, caching, deployment and scaling themselves.

Pros

Cons

Pricing: free and open source for self-hosted use, with custom enterprise pricing for hosted management.

Best for: engineering teams that need an open-source, self-hosted gateway and have the infrastructure team to operate it.

13. Qwen

Pick Qwen only as an open model to trial through a host, since the models rarely name it and never first.

Measured: named 8 of 50 (Qwen 16%), first 0 of 50 (Qwen 0%), average position 7.63.

Qwen is a minority mention, and the panel counts the names Qwen and Alibaba together. Its eight mentions come mostly from the Gemini and Claude models. No OpenAI model named it, and neither did Sonar Pro. For a buyer, Qwen is a model to test on a host. The counts give no case for building a product on it directly.

DataCamp lists Qwen among the popular open models that open-source API providers serve. It says those providers suit teams that want lower costs, more model flexibility and faster experimentation.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: open-model experimentation through a hosted provider.

14. xAI

Pick xAI only when its model behaviour or ecosystem matters to your product.

Measured: named 4 of 50 (xAI 8%), first 0 of 50 (xAI 0%), average position 5.5.

xAI is a niche pick in the recorded answers. Claude Opus 5 and Claude Fable 5 supplied three of its four mentions, and GPT-5.6 Sol the fourth. Sol’s reason for it was narrow: add it when its model behaviour or ecosystem is strategically useful. Four mentions from three models could shift with a single rerun of the panel, so treat the rank as provisional.

DataCamp lists xAI Grok among the partner models in Google Vertex AI’s Model Garden.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: products that depend on xAI’s particular model behaviour or ecosystem, as GPT-5.6 Sol framed it.

15. Cohere

Pick Cohere if enterprise retrieval is the product and you’ll test it against a frontier default on your own data.

Measured: named 1 of 50 (Cohere 2%), first 0 of 50 (Cohere 0%), average position 5.

Cohere’s single mention came from GPT-5.6 Sol’s comparison answer, which marked it for enterprise retrieval. That answer grouped it with Mistral and xAI as providers to add for a specific need. No other model named it. One mention says nothing about the product. It does say the models rarely think of Cohere when a developer asks what to build on. A team whose product is retrieval should run its own test before reading anything into the count.

DataCamp lists Cohere among the model providers inside Amazon Bedrock.

Pros

Cons

Pricing: no public pricing is recorded.

Best for: enterprise retrieval, as GPT-5.6 Sol framed it.

How the tools compare

LLM API providers give developers access to AI models without managing GPUs, model deployment, scaling or inference infrastructure. DataCamp sorts them into four groups: native LLM providers, open-source LLM API providers, LLM routing providers and cloud LLM providers.

The 15 names the models produced span all four groups, plus open model families such as Meta Llama and Qwen.

Answer share is the share of the 50 answers that named an API. Named first counts the answers where it appeared before any other tracked API. Average position is its place in the order of names, across the answers that named it. The pricing column summarises each entry’s cited pricing line.

Vendor Named Share Named first First share Avg position Pricing model
OpenAI 50/50 100% 33/50 66% 1.52 Per token, batch discount
Google 50/50 100% 2/50 4% 2.88 Per token
Anthropic 46/50 92% 14/50 28% 1.98 Not recorded
DeepSeek 33/50 66% 1/50 2% 4.45 Not recorded
OpenRouter 27/50 54% 0/50 0% 7.44 Prepaid credits at provider rates
Amazon Bedrock 26/50 52% 0/50 0% 6.88 Not recorded
Groq 25/50 50% 0/50 0% 6.04 Free tier, then per token
Azure OpenAI 24/50 48% 0/50 0% 7.17 Not recorded
Mistral 22/50 44% 0/50 0% 6.14 Not recorded
Together 19/50 38% 0/50 0% 7.16 Prepaid credits, per token
Meta Llama 17/50 34% 0/50 0% 6.29 No per-token fee self-hosted
LiteLLM 17/50 34% 0/50 0% 8.59 Free self-hosted, custom enterprise
Qwen 8/50 16% 0/50 0% 7.63 Not recorded
xAI 4/50 8% 0/50 0% 5.5 Not recorded
Cohere 1/50 2% 0/50 0% 5 Not recorded

OpenAI leads or ties every column. Below it, the table falls into three bands. OpenAI, Google and Anthropic appear in 46 or more answers. The nine APIs from DeepSeek to LiteLLM sit between 17 and 33 mentions. Qwen, xAI and Cohere trail with 8, 4 and 1. Only four APIs were ever named first, and DeepSeek’s single first placement is the only one outside the top three.

Where do the models disagree?

The models agree on the leader and split on almost everything below the top three. All ten named OpenAI and Google in 5 of 5 answers. Below that, the model a developer asks decides whether routers and open-model hosts appear at all.

Vendor GPT-5.6 Sol GPT-5.6 Terra GPT-5.6 Luna Claude Opus 5 Claude Sonnet 5 Claude Fable 5 Gemini 3.6 Flash Gemini 3.5 Flash Sonar Pro Sonar Reasoning Pro
OpenAI 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5
Google 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5
Anthropic 2/5 4/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5 5/5
DeepSeek 0/5 0/5 2/5 5/5 4/5 3/5 5/5 5/5 4/5 5/5
OpenRouter 0/5 0/5 0/5 3/5 4/5 5/5 5/5 5/5 2/5 3/5
Amazon Bedrock 4/5 2/5 2/5 3/5 3/5 3/5 3/5 1/5 2/5 3/5
Groq 0/5 0/5 0/5 3/5 4/5 3/5 5/5 4/5 2/5 4/5
Azure OpenAI 1/5 1/5 2/5 3/5 4/5 3/5 3/5 2/5 2/5 3/5
Mistral 1/5 0/5 4/5 2/5 3/5 4/5 1/5 3/5 1/5 3/5
Together 0/5 0/5 0/5 3/5 5/5 1/5 5/5 2/5 0/5 3/5
Meta Llama 0/5 0/5 0/5 1/5 2/5 0/5 5/5 4/5 0/5 5/5
LiteLLM 0/5 0/5 1/5 3/5 4/5 3/5 4/5 1/5 0/5 1/5
Qwen 0/5 0/5 0/5 2/5 1/5 0/5 3/5 1/5 0/5 1/5
xAI 1/5 0/5 0/5 2/5 0/5 1/5 0/5 0/5 0/5 0/5
Cohere 1/5 0/5 0/5 0/5 0/5 0/5 0/5 0/5 0/5 0/5

The sharpest split runs along model families. GPT-5.6 Sol, GPT-5.6 Terra and GPT-5.6 Luna named OpenRouter, Groq, Together and Meta Llama in none of their answers. Gemini 3.6 Flash named all four in 5 of 5. The Claude models sit between, naming OpenRouter in 3 to 5 answers each.

The OpenAI models do name alternatives. GPT-5.6 Sol named Amazon Bedrock in 4 of 5 answers, and GPT-5.6 Luna named Mistral in 4 of 5. They reached for clouds and model makers. Across their 15 answers, the only router they named was LiteLLM, once.

The answer text suggests why. The OpenAI models fill the budget slot with OpenAI’s own smaller models, so they have no gap for a router or a low-cost host to fill. GPT-5.6 Terra laid out the ladder in its answer to the startup prompt:

make GPT‑5.6 Terra the initial “quality default,” while using GPT‑5.6 Luna for high-volume, lower-risk tasks and escalating hard requests to GPT‑5.6 Sol

The Gemini and Claude answers fill the same slot with other vendors. Gemini 3.6 Flash called for an abstraction layer or router alongside one or two direct APIs. Their sources lean the same way. GPT-5.6 Terra, Sol and Luna answers repeatedly open their citations with OpenAI’s own pages. Two cited Claude Opus 5 answers open with third-party pricing and comparison posts instead. The split is measured. The reason is a reading of the answer text, and the panel doesn’t test it.

Family loyalty is weaker than the split might suggest. Every model named Google in every answer, so Google’s own models show no extra lean toward it. The Claude models named Anthropic in every answer, and so did both Google models and both Perplexity models.

What should a buyer do with the counts?

Start with a first-party API and keep the provider replaceable. OpenAI is the default the models converge on. Anthropic is the pick when coding or agents drive the product. Gemini is the second provider most answers pair with either.

For a startup balancing cost and quality, most answers to that prompt converge on a pattern rather than a single vendor: a default model, cheaper models for routine traffic and escalation for hard requests. Claude Opus 5 told a startup to pick a default mid-tier model and route around it. GPT-5.6 Sol told developers to keep the model provider replaceable.

MongoEngine advises abstracting LLM calls behind a thin client layer from day one to avoid lock-in.

Add a router when you need more than a couple of models, and pick the kind of router by who will run it.

Braintrust places OpenRouter with developers who want broad model access through a single account. It places LiteLLM with engineering teams that need a self-hosted gateway and can operate it.

Check any AI-built shortlist against a second assistant family. A developer who asked only a GPT-5.6 model would have seen OpenRouter, Groq and Together in no answer. A developer who asked Gemini 3.6 Flash would have seen all three every time.

Then test the shortlist on your real workload before you commit. MongoEngine’s advice is to start with one provider, benchmark it against your actual workload, and switch or split traffic once you have real performance data.

How the sample was built

10 models x 5 fixed prompts = 50 recorded answers. Each model answered each prompt once, in the 2026-09 edition, with web search switched on. The five prompts, verbatim:

  1. What is the best LLM API to build a product on for a developer? Name specific products.
  2. Which LLM API to build a product on would you recommend to a developer in 2026?
  3. Compare the top LLM API to build a product on options right now.
  4. I’m a developer and I need a LLM API to build a product on. What should I use and why?
  5. Best LLM API to build a product on for an AI startup balancing cost and quality?

The ten models come from four families. OpenAI supplied GPT-5.6 Sol, GPT-5.6 Terra and GPT-5.6 Luna, for 15 answers. Anthropic supplied Claude Opus 5, Claude Sonnet 5 and Claude Fable 5, for 15 answers. Google supplied Gemini 3.6 Flash and Gemini 3.5 Flash, for 10 answers. Perplexity supplied Sonar Pro and Sonar Reasoning Pro, for 10 answers.

The answers cited syncfusion.com 140 times, more than any other host, and braintrust.dev 88 times. Every recorded answer, the per-model split and the cited sources sit in the LLM API category record. The method sets out how names are matched and counted.

How this sits against the LLM API guides

The guides that rank for this question rank products for purchase. The panel counts which names AI answers produce. Four captured results show the difference.

Braintrust ranks seven unified LLM API providers and puts its own Braintrust Gateway first. The rest of its list is OpenRouter, Vercel AI Gateway, LiteLLM, Portkey, Together AI and Groq. Its calls to action point to a Braintrust sign-up.

That publisher shows up inside the answers too. The domain braintrust.dev was the second most-cited host in the recorded answers, and a Sonar Reasoning Pro answer named Braintrust Gateway as its example of a multi-provider gateway to build on. Claude Opus 5 noticed the pattern in its own search results:

“the results I got back are mostly SEO-driven listicles and vendor blogs”

For a buyer, that means an AI answer can inherit a vendor-authored guide’s ordering. It’s one more reason to read the per-model split rather than a single assistant’s list.

MongoEngine compares four APIs: OpenAI, Anthropic, Google Gemini and Meta Llama. It weighs latency, context window size, rate limits, pricing per token and uptime SLAs. Its pricing examples are dated and use earlier model generations. It closes by pointing Python developers to the MongoEngine ecosystem.

DataCamp ranks ten providers on model quality, developer experience, pricing, scalability, ecosystem support and production readiness. Its list includes Fireworks AI, Nebius AI, Requesty.ai and Google Vertex AI. The captured page also promotes DataCamp’s own AI engineering track.

The panel tracks none of Fireworks AI, Nebius AI or Requesty.ai, and has no separate entry for Vertex AI.

The fourth result is a conference talk in which Dan Erez shows how to expose LLMs as APIs using vLLM and OpenLLM.

None of the four counts what AI models say, and none splits a result by model. The per-model split adds both. It shows the router tier appearing or vanishing depending on which assistant a developer asks.

What can these counts not tell you?

The counts can’t tell you which API is better. Answer share and named first count presence in recorded answers. They say nothing about quality, uptime, support, pricing fairness or fit with your stack. Being named also differs from being recommended, since an answer can list an API only to warn against it.

Each model gave one answer per prompt, so a rerun could shift a small count. The figures are one dated snapshot, the 2026-09 edition. Names are matched as strings, so a passing “Google”, “Meta” or “Together” counts for that vendor, and an answer that names a GPT-5 or Claude model counts for its maker. The panel collected answers through each model’s API, and consumer chat apps such as ChatGPT can answer differently. All five prompts are in English.

Frequently asked questions

Which AI API is best for building APIs?

Read as a question about which LLM API to build a product on, the models’ answer is OpenAI, with Anthropic as the one other API they regularly put first. If the question means serving your own model as an API, that’s a different job.

Dan Erez’s API Conference talk shows how to expose LLMs as APIs using vLLM and OpenLLM.

What are the best LLM APIs?

Ranked by how often ten AI models name them, the top six are OpenAI, Google, Anthropic, DeepSeek, OpenRouter and Amazon Bedrock. The order reflects visibility in AI answers. It doesn’t rank performance.

Is there an API for LLM?

Yes. LLM API providers host models and let developers send a request and receive a response without self-hosting.

LLM routing providers go a step further and give access to multiple models and providers through one API.

What is the best free API for developers?

The panel doesn’t measure price, so it can’t rank free options. The captured guides record several free routes.

OpenRouter offers free models with rate limits. Groq has a free tier with rate limits. LiteLLM is free and open source for self-hosted use.

Self-hosted Llama carries zero per-token cost. Running it at production latency needs dedicated GPUs.

Should a product use one LLM API or several?

Start with one and keep the option of more. GPT-5.6 Sol and GPT-5.6 Luna both advised building so the provider can be swapped.

Most unified API providers now expose an OpenAI-compatible endpoint, so teams can keep their existing client when they add or test a new model.

Can a vendor pay for a higher position?

No. Positions come from counts in the recorded answers. Vendors can’t pay to appear, be reordered or be removed.

How this list is ordered

The order is the measurement, not an assessment of the products. Answer share is the share of recorded answers that named the tool. Named first is the share where it appeared before any other tracked tool. Both are counts from one dated edition and are published in full on the category page.

A tool appears here only if it was named in the edition and its record carries a sourced claim. A product that was never named is not listed, and no position is sold.

Where to check it