10 Best LLM APIs of 2026

The OpenAI API ranks first because it covers the most ground from one account. The pricing page lists four GPT-6 tiers: Astra at $10 input and $50 output per 1M tokens for the hardest work, GPT-6.1 Sol at $2 and $10 for coding and agents, GPT-6.1 Terra at $1 and $5 for visual reasoning and computer use, and GPT-6 Luna at $0.10 and $0.50 for high-volume tasks. The same platform sells image generation, realtime voice, live transcription and translation, plus tools such as web search and hosted containers, so a team can build a whole product without a second vendor. Batch processing cuts input and output prices by 50% for jobs that can wait up to 24 hours, and cached input is billed at a tenth of the input rate or less. The trade-offs are cost at the top end and the lack of a listed free tier. Teams with strict data location needs also pay a 10% uplift for regional processing.

The Anthropic API ranks third, close behind Gemini, with the clearest pricing page on the list: every Claude model, cache write and cache hit price sits in one USD table. Claude Fable 5.1 targets demanding reasoning and long-horizon agent work at $10 input and $50 output per 1M tokens. Opus 5.5 is $4 and $20 and is described for long-running agentic coding, Sonnet 5.5 is $2 and $10, and Haiku 5.5 is $0.10 input for prompts up to 100,000 tokens, rising to $0.50 above that. Prompt caching has 5-minute and 1-hour write options, and cache hits are a small fraction of input cost, which matters for agents that resend long context. The models are also sold through Amazon Bedrock and Google Cloud, so teams can buy through an existing cloud contract. Watch the tokenizer note: newer models produce about 30% more tokens for the same text, which raises the effective price.

The Gemini API ranks second because it pairs strong models with the most useful free tier among the large labs. Google AI Studio gives developers free input and output tokens on many models, which is enough to build and test a prototype, though free tier content is used to improve Google products. The paid tier adds higher rate limits, context caching and the Batch API at half price. Gemini 4 Argon is the flagship for complex engineering and long-decode output up to 1M tokens, at $2 input and $10 output per 1M tokens through November 30, 2026, doubling on December 1. Gemini 3.8 Flash is $0.75 input through the end of 2026, and Gemini 2.5 Flash-Lite is $0.10. Grounding with Google Search and Maps is built in, with 5,000 free requests a month before per-request charges. Several prices are promotional and scheduled to rise, so budget against the later rates.

Fireworks AI ranks fifth and has the lowest starting price we found, $0.05 per 1M input tokens on its smallest highlighted model. Its pricing page explains a useful idea: every serverless request can pick a mode. Standard is the default, Priority holds up at peak load, Fast targets 100 or more tokens per second, Batch is 50% off, and Spot is 30% off on best-effort capacity that can drop to zero. Cached input is billed at half price or less with no write fee. The highlighted catalog includes Kimi K3 at $3 input, GLM 5.3 at $1.40, DeepSeek V4.1 Flash at $0.30 and GPT OSS 120B at $0.15. For steady traffic, Reserved Throughput adds an SLA and 99.9% uptime terms. The catches are that serverless runs on prepaid credits and US-only processing adds a 50% premium on select models.

Groq ranks fourth on speed and price. Its models page lists tokens per second next to each price, which few vendors do: GPT OSS 20B runs at 1,000 tokens per second for $0.075 input and $0.30 output per 1M tokens, and GPT OSS 120B at 500 tokens per second for $0.15 and $0.60. Both have 131,072-token context windows. The API is OpenAI compatible, so moving an existing app is mostly a base URL change, and there are Free Plan and Developer Plan rate limits plus Batch and Flex tiers. The weakness is choice. The production list is short, and several models such as Llama 3.3 70B and Muse Spark Open are marked Enterprise with Contact Sales pricing. Groq does not sell frontier proprietary models, so it works best alongside a larger provider for tasks where response time matters most.

Together AI ranks sixth, just behind Fireworks AI on value, and is the broadest open model platform here. The pricing page lists serverless models for chat, vision, image, audio, video, embeddings and rerank, with input, cached input and output prices per 1M tokens: GLM-5.3-Flash at $0.15 input and $0.50 output, MiniMax M3.1 Flash at $0.15 and $0.60, DeepSeek V4.1 Flash 1017 at $0.30 and $1.20, and Kimi K3 at $3 and $15. Beyond serverless, Together sells batch inference, provisioned throughput units, dedicated endpoints for custom models, fine-tuning priced per training token and GPU clusters. That makes it a good home for a team that starts on a hosted model and later wants to fine-tune or run its own. The downside is the size of the menu, and like Groq and Fireworks it does not offer frontier proprietary models.

The DeepSeek API ranks seventh with the highest value score on the list. Its pricing doc lists two models with a 1M-token context: deepseek-flash, now served by DeepSeek-V4.1-Flash-1017, at $0.30 input and $1.20 output per 1M tokens at peak, and deepseek-v4-pro at $1.32 and $3.96. Cache hits on Flash cost $0.006 per 1M tokens, and every rate halves in off-peak hours, which cover weekends and all weekday hours outside the two UTC peak windows. The API accepts both OpenAI and Anthropic request formats, so tools built for either SDK can switch with a base URL change, and both models support JSON output, tool calls and the Responses API. The limits are scope and options: there are only two models, Pro has no vision input, and there is less enterprise packaging than on Azure or Bedrock.

The Mistral API ranks eighth. Its strongest point is open weights: Mistral Large 4 is described as an open-weight multimodal model and Small 4 is released under Apache 2.0, so teams can start on the hosted API and later run the same model themselves. Mistral's pricing page says most models are priced per million tokens and gives Mistral Large as the example at $0.50 input and $1.50 output, which is low for a flagship. Batch processing cuts prices by 50% and cached input tokens cut input cost by up to 90%. OCR, Voxtral speech and tool APIs are priced per page, minute or call. The docs also list hosted third-party models such as GLM 5.3 and DeepSeek V4.1 Flash. The weaker part is transparency: the public pricing page focuses on the Vibe subscription and gives Mistral Large as its only API price example.

Azure OpenAI ranks ninth because it trades ease of use for enterprise controls. The pricing page shows OpenAI's GPT-6 and GPT-6.1 series at Global rates that match OpenAI's own, with GPT-6 Luna short context at $0.10 input and $0.50 output per 1M tokens. Data Zone deployments keep processing inside the EU or US for a premium, $0.11 input on Luna, and Regional deployments cover up to 27 regions. Microsoft also sells provisioned throughput units with monthly and annual reservations and a Batch API at 50% off Global Standard pricing. For a company already on Azure, this means OpenAI models under an existing Microsoft agreement, network rules and billing, with links into Fabric, Cosmos DB and Azure AI Search. For a small team it is more work: you create resources and deployments before the first call, and the page notes that prices are estimates that depend on your agreement.

Amazon Bedrock ranks tenth on our weights, which reward ease of use, but it has the broadest catalog here. The pricing page lists models from AI21 Labs, Amazon, Anthropic, Cohere, DeepSeek, Google, Meta, MiniMax, Mistral AI, Moonshot AI, NVIDIA, OpenAI, Qwen, Writer, xAI and others, sold through one AWS service. Standard, Flex, Priority and Reserved tiers let you trade cost against speed and capacity, and select models get batch inference at 50% lower prices than on-demand. Bedrock also sells Knowledge Bases, Guardrails, model evaluation, prompt routing and custom model import, so it suits teams that want the model layer inside existing AWS security and billing. The cost is complexity: model access, IAM roles and regions come first, and per-model prices are loaded dynamically, so we could not confirm a USD figure from our location. Anthropic's docs also note a 10% premium on regional endpoints for newer Claude models.