RadarTrek Intel — monthly score updates
We track 40+ tools so you don't have to. Score changes, new tools, and new guides — once a month, no spam.
6 tools scored · 6 dimensions · Last reviewed June 2026
An AI API gives your application raw access to a large language model — text generation, reasoning, and increasingly image/audio understanding — billed per token rather than as a finished product.
The raw model API market moves faster than almost any other category we track: pricing per million tokens drops regularly, context windows keep expanding, and the "best" model for a given task shifts every few months. OpenAI remains the default for the broadest third-party tooling support, Anthropic's Claude models are consistently praised for reasoning depth and reliable instruction-following, Google's Gemini leads on context window size and native multimodal input, and Groq stands apart entirely on raw inference speed thanks to custom hardware. Mistral and Together AI serve the growing segment of teams who want open-weight models with more pricing and deployment flexibility.
What are you looking for?
I want the broadest tooling and ecosystem support
The OpenAI API has the largest third-party integration ecosystem of any provider — if you're evaluating frameworks, libraries, or no-code tools, OpenAI compatibility is usually assumed as the baseline.
See ranked list →
I need strong reasoning and reliable instruction-following
The Anthropic API (Claude models) is consistently rated for careful reasoning, instruction adherence, and reliable tool use — important for agentic workflows where mistakes compound.
See ranked list →
Latency is critical — I'm building something real-time
Groq runs models on custom LPU hardware at inference speeds far beyond GPU-based providers, making it the clear choice for latency-sensitive use cases like voice agents where every hundred milliseconds is felt by the user.
See ranked list →
Radar comparison — select up to 5 tools
Click radar to enlarge · Click tool names to see full breakdown
Click any column to sort
| Weighted average | Overall | Reasoning depth, accuracy, and instruction following. | Cost per million tokens relative to capability. | Time to first token and tokens-per-second throughput. | Maximum tokens the model can process in one request. | Support for image, audio, and video inputs. | SDK quality, documentation, and tool-use support. | Monthly |
| ★Google Gemini API Multimodal-first models with massive c… | 87 | 88 | 85 | 80 | 98 | 95 | 82 | Free usage |
| Anthropic API Claude models — strong reasoning and l… | 83 | 95 | 72 | 75 | 90 | 80 | 92 | Free usage |
| OpenAI API The default choice — broadest tooling … | 83 | 92 | 70 | 78 | 80 | 92 | 95 | Free usage |
| Mistral API European open-weight models with stron… | 80 | 78 | 90 | 82 | 72 | 60 | 78 | Free usage |
| Groq The fastest inference speed in the mar… | 78 | 75 | 88 | 99 | 65 | 30 | 80 | Free usage |
| Together AI Run any open-source model with fine-tu… | 76 | 75 | 85 | 80 | 70 | 55 | 75 | Free usage |
Click tool names to see the full radar breakdown · Open screener for advanced filtering
Output Quality
Reasoning depth, accuracy, and instruction following.
Price / Value
Cost per million tokens relative to capability.
Latency
Time to first token and tokens-per-second throughput.
Context Window
Maximum tokens the model can process in one request.
Multimodal
Support for image, audio, and video inputs.
Developer UX
SDK quality, documentation, and tool-use support.
New to AI APIs? Start here
Download the cheat sheet
All 6 tools scored across 6 dimensions — one printable page.
Get score updates for this category
We'll email you when scores change or new AI APIs tools are added — monthly max, no spam.
No spam. Unsubscribe any time.
For most general-purpose applications, OpenAI or Anthropic are the safest starting points given their tooling maturity and documentation depth. If reasoning quality and reliable instruction-following matter most (agentic tasks, complex multi-step workflows), Anthropic's Claude models tend to score strongest. If raw ecosystem breadth matters most, OpenAI has the edge.
Groq runs inference on custom LPU (Language Processing Unit) chips designed specifically for the sequential nature of language model inference, rather than general-purpose GPUs. This architectural difference, not just better optimisation, is what produces the large speed gap — it matters most for latency-sensitive use cases like real-time voice agents.
An AI API (OpenAI, Anthropic, Gemini, etc.) is the raw model accessed programmatically for building into your own application — billed per token, with full control over prompts and integration. A consumer AI tool like ChatGPT Plus is a finished product built on top of a model, priced as a flat monthly subscription for an end user, not a developer.
Open-weight models (via Together AI, Mistral, or Groq) offer more deployment flexibility — including self-hosting — and can be considerably cheaper at scale, especially for well-defined tasks that don't need frontier-model reasoning. Closed models (OpenAI, Anthropic, Gemini) generally still lead on the hardest reasoning and instruction-following tasks.
Pricing is per million input/output tokens and varies enormously by model tier — a small, fast model can cost a fraction of a cent per request, while a frontier reasoning model can cost several dollars per million tokens. Always check current pricing pages directly, as these rates change frequently as new model generations launch.
How these scores are calculated
AI API scores are based on published benchmark performance (reasoning, instruction-following), per-million-token pricing across input and output, measured time-to-first-token and throughput, maximum context window, and multimodal input support as of 2026.
Full methodology →Want this built for your business?
We design and build digital products — web apps, AI tools, SaaS platforms.