10 Best LLM APIs of 2026

The Gemini API is the strongest all-round pick for teams that care about price per token and context length at the same time. Google publishes a free tier where input and output are free of charge on many models, so a prototype can run without a card. Paid prices are low: Gemini 2.5 Flash-Lite costs $0.10 per 1M text input tokens and Gemini 3.5 Flash-Lite $0.30 in and $2.50 out, while the flagship Gemini 4 Argon offers a 1M-token context and long decodes. Each model has Standard, Batch, Flex and Priority modes, and tools such as Google Search grounding include 5,000 free requests a month on Gemini 3 and newer models before per-request fees. Rate limits follow tiers: Tier 1 needs a linked billing account, Tier 2 needs $100 paid plus 3 days, and Tier 3 needs $1,000 plus 30 days. The trade-off is complexity: there are many models and modes, thinking tokens are billed, and the top model is paid only.

The OpenAI API remains the most complete platform for building production features, and its price ladder now runs from very cheap to premium. GPT-6 Luna costs $0.10 per 1M input tokens and $0.50 per 1M output tokens, GPT-6.1 Terra $1 and $5, GPT-6.1 Sol $2 and $10, and GPT-6 Astra $10 and $50. The models page lists a 1.05M-token context window and 128K maximum output across the GPT-6 family. Built-in tools include web search, file search and computer use, and the platform adds realtime voice, transcription and image models. Cost controls are good: the Batch API cuts input and output prices by 50%, flex processing trades speed for price, and you can set a monthly budget per project. Rate limits rise through Free, Build, Launch and Grow tiers as credit purchases pass $5, $100 and $500. Note that standard rates apply under 272K context, and there is no free API tier.

Anthropic's API is the strongest choice for long-context and agentic work where per-token price is not the only factor. Claude Haiku 5.5 costs $0.10 per 1M input tokens and $0.50 per 1M output tokens for prompts up to 100,000 tokens, Claude Sonnet 5.5 costs $2 and $10, and Claude Opus 5.5 $4 and $20. The models overview lists a 1M-token context window and 128K max output on current models, and Claude 4.6 and later models other than Haiku 5.5 bill the full 1M window at standard rates. Prompt caching charges cache hits at 5% of the input price, the Batch API halves prices, and the two discounts stack. Code execution is free when paired with web search or web fetch. Rate limits are set by usage tier, with monthly spend caps of $500 on Start, $1,000 on Build and $200,000 on Scale, which new apps can outgrow quickly. Haiku 5.5 input also rises from $0.10 to $0.50 per 1M tokens above 100,000-token prompts.

DeepSeek's API is the value pick for teams that want long context without paying frontier prices. The deepseek-flash model costs $0.30 per 1M input tokens on a cache miss and $1.20 per 1M output tokens at peak, and off-peak rates are half that. Cache hits drop input to $0.006 per 1M tokens at peak and $0.003 off-peak. Both deepseek-flash and deepseek-v4-pro support a 1M-token context and up to 384K output, with JSON output, tool calls and a thinking mode. Base URLs in OpenAI and Anthropic formats let existing SDKs and agent tools point at DeepSeek with a config change. Instead of token-per-minute tiers, DeepSeek publishes concurrency limits of 2,500 for Flash and 500 for V4 Pro. The catches: peak hours fall on weekday mornings UTC, so bills depend on timing; V4 Pro has no vision support; and the lineup is small next to OpenAI or Google.

Groq is the speed specialist on this list. GroqCloud runs open models on Groq's own LPU hardware and publishes a speed figure for each one: about 500 tokens per second for GPT OSS 120B and 1,000 for GPT OSS 20B. Prices are low, at $0.15 in and $0.60 out per 1M tokens for GPT OSS 120B and $0.075 and $0.30 for GPT OSS 20B. Groq is also clear on rate limits. The models page lists developer plan limits of 250K tokens per minute and 1K requests per minute for the GPT OSS models, and the rate limits page lists a free plan with its own caps, such as 30 RPM and 200K tokens per day on GPT OSS 20B. The API is OpenAI compatible and supports batch and flex service tiers. The limits are context, around 131K tokens on GPT OSS, and model choice: there are no closed frontier models, and some catalog models are sales-only.

xAI keeps its API simple: Grok 4.7 is the recommended model for code, chat and everything else outside media. It costs $2.00 per 1M input tokens and $6.00 per 1M output tokens with a 500K-token context and configurable reasoning, which makes output cheaper than most flagship rivals. Server-side tools cover web search, X search, code execution and remote MCP, and the platform adds a batch API, deferred completions, prompt caching and regional endpoints. A Voice API and Imagine API handle speech, images and video. Rate limits scale through tiers based on cumulative spend since January 1, 2026: Tier 0 at $0, then $50, $250, $1,000 and $5,000, and xAI says tiers never downgrade. The gaps are a missing budget model for high-volume tasks and a context window half the size of the leaders. Grok also has no knowledge of current events unless search tools are on.

Fireworks AI is a production inference platform for open models, and it lists the lowest verified serverless price on this list: NVIDIA Nemotron 3.5 Lightning 30B A3B at $0.05 per 1M input tokens and $0.20 per 1M output tokens. Larger models are priced in the same table, such as GPT OSS 120B at $0.15 and $0.60 or Kimi K3 at $3.00 and $15.00, each with a cached input rate and a priority rate. Spot capacity runs 30% below standard on select models, and US-only serverless costs a 50% premium where offered. When traffic is steady, Fireworks offers on-demand GPUs and reserved capacity with a throughput SLA and 99.9% uptime terms. Account quotas follow spending tiers, from a $50 monthly limit on Tier 1 to $50,000 on Tier 4. The caveats are spot capacity that can fall to zero, frequent catalog changes and context limits that vary by model.

Mistral AI is the European option on this list and the one most focused on open weights. Its pricing FAQ gives Mistral Large at $0.50 per 1M input tokens and $1.50 per 1M output tokens, a low price for a flagship model, and says batch processing cuts prices by 50% and cached input by up to 90%. The models overview lists Mistral Large 4 as an open-weight general-purpose multimodal model and Mistral Small 4 under Apache 2.0, so teams can move a workload to their own hardware later. The same platform carries OCR, Voxtral speech and moderation models, and Mistral Studio tracks API calls and spend. Mistral also sells consumer and coding plans under the Vibe brand. The weak spots for a developer comparing APIs are documentation gaps: the pages we checked did not state context windows for the main models or publish rate limit tiers, and several model versions have retirement dates within months.

Together AI fits teams that want open models and the infrastructure around them in one account. Its pricing table covers models from Z.ai, Moonshot, MiniMax, Qwen, NVIDIA and DeepSeek, with GLM-5.3-Flash at $0.15 per 1M input tokens and $0.50 per 1M output tokens, DeepSeek V4.1 Flash at $0.30 and $1.20, and Kimi K3 at $3.00 and $15.00. Most rows also list a cached input price. Beyond serverless, Together sells batch inference, provisioned throughput with SLAs, dedicated inference, fine-tuning and GPU clusters, so a team can train and serve on the same platform. On rate limits, Together's docs say most users can use serverless inference without hitting them, with limits only under high volume or bursts and custom limits on high-demand models. The downsides are a long, shifting price list and context limits that vary by model.

Amazon Bedrock is the aggregator choice for companies that run on AWS. AWS says Bedrock gives access to hundreds of foundation models from leading AI companies and powers generative AI for more than 100,000 organizations. Instead of signing a contract with each model vendor, a team calls models through AWS with its existing IAM roles, logging and billing. Bedrock adds agent tooling through AgentCore, a managed agents option powered by OpenAI models, and automated reasoning checks to flag incorrect answers. For cost, AWS lists model distillation, prompt caching and intelligent prompt routing, and says routing can cut costs by up to 30%. AWS also states that Bedrock never stores or uses your data to train models. The trade-offs are setup weight, since you work through AWS accounts and regions, and price transparency: per-model prices did not load as text on the pricing page from our location, so we list them as pending.