The best inference API in 2026 depends on what you optimize. OpenAI is the strongest default for frontier coding and product work, Anthropic Claude is best for agents and long reasoning, Gemini is best for long context and Google-grounded apps, Groq is best for ultra-low-latency open models, Together AI and Fireworks AI are the strongest open-model serving platforms, OpenRouter is the best gateway, NVIDIA NIM is best for prototype-to-self-hosted deployment, AWS Bedrock is the safest enterprise procurement layer, and Baseten is best for custom model APIs.
What is an inference API?
An inference API is the endpoint your application calls when it needs an AI model to generate, classify, embed, route, search, transcribe, reason, call tools, or return structured output. In practice, the inference API is where AI becomes a production cost center: every retry, long context, tool loop, cached prompt, batch job, region requirement and latency target affects the bill.
The right question is not “which provider has the cheapest tokens?” The right question is “which provider gives the lowest cost per successful task at the quality, latency, security and compliance level this product needs?” For the architecture around that decision, read AI Architecture for Production. For NVIDIA-specific pricing and free-tier limits, see NVIDIA NIM API Pricing.
Most serious AI products should not use one inference API. They should route: frontier model for hard tasks, cheap open-model API for routine work, fallback provider for outages, and batch route for low-priority jobs.
Best inference APIs compared
| Rank | Provider | Best for | Representative public pricing | Main trade-off |
|---|---|---|---|---|
| 1 | OpenAI API | Frontier product default, coding, agents, structured output | GPT-5.5: $5 input / $30 output per 1M tokens | Premium output cost and hosted-tool lock-in |
| 2 | Anthropic Claude API | Agents, long reasoning, coding, prompt caching | Opus 4.6: $5 / $25; Sonnet 4.5: $3 / $15 per 1M tokens | Higher cost than open-model APIs |
| 3 | Google Gemini API | Long context, multimodal input, Search/Maps grounding | Gemini 2.5 Pro: $1.25-$2.50 input / $10-$15 output per 1M tokens | Pricing varies by context length and tier |
| 4 | Groq | Low-latency open-model inference | GPT OSS 120B: $0.15 input / $0.60 output per 1M tokens | Narrower catalog than aggregators |
| 5 | Together AI | Open-model breadth, batch, dedicated endpoints | gpt-oss-120B: $0.15 / $0.60; Qwen3 235B: $0.20 / $0.60 | Quality depends heavily on model choice |
| 6 | Fireworks AI | Production open-model serving and on-demand GPUs | Serverless per token; H100/H200 on-demand at $7/hour, B200 at $10/hour | Best economics require GPU utilization work |
| 7 | OpenRouter | One API for many models and providers | Model-dependent marketplace/pass-through pricing | Extra abstraction layer and provider variance |
| 8 | NVIDIA NIM | Free prototyping and self-hosted NVIDIA-optimized path | Hosted prototype endpoints free/rate-limited; AI Enterprise from $4,500/GPU/year | Not a simple production per-token SaaS API |
| 9 | AWS Bedrock | Enterprise procurement, guardrails, regions, AWS stack | Model/provider/region-specific; batch can be 50% lower than on-demand | Complex pricing surface |
| 10 | Baseten | Custom/fine-tuned model APIs and dedicated deployments | GPT OSS 120B: $0.10 input / $0.50 output per 1M tokens | More infra-oriented than simple app APIs |
The 2026 inference API market map
Provider-by-provider recommendations
1. OpenAI API – best default for frontier product work
OpenAI remains the broadest default for teams that want one API to cover many product surfaces. Its official pricing page lists GPT-5.5 as the flagship model for coding and professional work, with GPT-5.4 and GPT-5.4 mini as cheaper routes. The platform also has Batch API discounts, Flex processing, Priority processing, data residency and reserved capacity, which matter once traffic becomes predictable.
Choose OpenAI when quality and product breadth matter more than the lowest token price.
2. Anthropic Claude API – best for agents and long work
Claude is the strongest pick when the task is not one message but a loop: plan, call tools, read output, revise, try again. Anthropic’s pricing page lists Opus 4.6 at $5 input and $25 output per million tokens, and Sonnet 4.5 at $3 input and $15 output. Prompt caching is the key economic feature: repeated repo, policy, or knowledge-base context can become much cheaper on cache reads.
Choose Claude when the model has to stay coherent over many tool calls and revisions.
3. Google Gemini API – best for long context and grounding
Gemini is strongest when context length and Google-native grounding matter. The official Gemini API pricing page separates free tier, paid tier, context length bands, grounding, live API and batch pricing. Gemini 2.5 Pro paid pricing is listed at $1.25 input and $10 output per million tokens up to 200k tokens, then $2.50 input and $15 output above 200k tokens. That split matters for long-document products.
Choose Gemini when context window, multimodality or Google grounding is the core requirement.
4. Groq – best for low-latency open-model inference
Groq is the latency specialist. It is not trying to be the biggest model catalog; it is trying to make supported models feel instant. Its pricing page lists several open models at very low per-token prices, including GPT OSS 120B at $0.15 input and $0.60 output per million tokens. If user experience is shaped by response speed, Groq deserves a route in your stack.
Choose Groq when milliseconds matter more than having every possible model.
5. Together AI – best open-model breadth
Together AI is the best all-around open-model platform for teams that want breadth, not just one fast endpoint. Its pricing page lists serverless models, image models, batch discounts, dedicated endpoints, fine-tuning and code interpreter pricing. Representative listed prices include gpt-oss-120B at $0.15 input and $0.60 output, and Qwen3 235B at $0.20 input and $0.60 output per million tokens.
Choose Together AI when you want to compare and deploy many open models from one API.
6. Fireworks AI – best production open-model serving
Fireworks AI is strongest when you are past experimentation and care about serving economics. Its official pricing covers serverless models and on-demand deployments. Public on-demand GPU pricing lists H100/H200 at $7/hour and B200 at $10/hour, which makes Fireworks attractive for teams that can keep GPUs busy or need dedicated deployments.
Choose Fireworks when open-model throughput and deployment control matter more than a one-line SaaS price.
7. OpenRouter – best model gateway
OpenRouter is a routing layer rather than a single-model provider. It gives developers one OpenAI-compatible API for many model providers, with model-specific pricing and routing behavior. That is valuable when you are testing models, building a model-router, or want fallback routes without writing ten provider integrations yourself.
Choose OpenRouter when speed of experimentation matters more than owning each provider relationship directly.
8. NVIDIA NIM – best prototype-to-self-hosted path
NVIDIA NIM is not priced like a normal public token API. Hosted endpoints on build.nvidia.com are free for prototyping under Developer Program access, but rate-limited. Production runs under NVIDIA AI Enterprise, which NVIDIA’s FAQ lists from $4,500 per GPU per year or about $1 per GPU hour in cloud. NIM is best understood as a bridge from API experimentation to self-hosted or enterprise GPU inference.
Choose NVIDIA NIM when you want to test for free now and potentially run optimized inference under your own control later.
9. AWS Bedrock – best enterprise procurement layer
AWS Bedrock is not the cheapest or simplest inference surface, but it is often the easiest path through enterprise procurement. Pricing depends on model, provider, region and mode, with on-demand, batch, provisioned throughput and custom model paths. Bedrock is compelling when you already run AWS security, logging, IAM, data residency and billing.
Choose Bedrock when the buyer is an enterprise security team before it is an ML team.
10. Baseten – best custom model API platform
Baseten sits between managed inference API and infrastructure platform. Its pricing page lists model API pricing, dedicated deployments, self-hosting and training. Representative listed API pricing includes GPT OSS 120B at $0.10 input and $0.50 output per million tokens. It is especially useful when your model is not simply “call the latest frontier API” but a fine-tuned or custom deployment that still needs a production API surface.
Choose Baseten when you need custom model hosting without building the entire serving platform yourself.
Which inference API should you pick?
For most startups: start with OpenAI or Claude as the premium route, then add Groq, Together AI or Fireworks for cheap/fast open-model routes. For enterprise teams: start with AWS Bedrock if procurement blocks direct vendor APIs, then add direct OpenAI/Anthropic routes only where product quality demands it. For AI infrastructure teams: evaluate NVIDIA NIM, Fireworks and Baseten because GPU utilization and data control may matter more than simple token pricing.
For consumer chat or voice: put Groq or another low-latency open-model route in the stack. For coding agents: route hard tasks to Claude or OpenAI and use cheaper models for classification, summarization and test-log compression. For RAG/search: cost is usually dominated by retrieval, reranking and repeated context, so prompt caching and context-window pricing matter more than headline model intelligence.
The cost model that actually matters
Token price is only one input. A production inference budget should track:
- Cost per successful task: include retries, failed generations, validation loops and human escalation.
- Latency per completed workflow: not just first-token latency, but tool calls, retrieval, reranking and final validation.
- Cache hit rate: prompt caching can change Claude/OpenAI economics dramatically on repeated context.
- Batch share: offline jobs should use Batch or flex/async routes where possible.
- Fallback cost: the cheap route must fail over before it burns user trust.
- Data boundary cost: EU data residency, VPC, self-hosting or Bedrock-style procurement can dominate token savings.
Do not standardize on the cheapest API before measuring task success. A $0.60/M output model that needs four retries can be more expensive than a $15/M output model that succeeds once.
FAQ
Where this fits: an inference API is the layer where your model actually runs and where you pay per token. Above it sit the tools that consume it — see best AI coding assistants 2026 and best AI agent frameworks 2026. For NVIDIA’s self-host path specifically, see the NVIDIA NIM pricing & limits guide.
What is the best inference API in 2026?
OpenAI is the best default for broad frontier product work, Anthropic Claude is best for agents and long reasoning, Gemini is best for long context and Google-grounded apps, Groq is best for low latency, Together AI and Fireworks are best for open-model serving, OpenRouter is best as a gateway, NVIDIA NIM is best for prototype-to-self-hosted workflows, AWS Bedrock is best for enterprise procurement, and Baseten is best for custom model APIs.
Which inference API is cheapest?
For published token prices, open-model providers such as Groq, Together AI, Fireworks and Baseten are usually cheaper than frontier APIs. But the cheapest listed token price is not always the cheapest production cost. Measure cost per successful task after retries, latency, cache hit rate, output length and fallback behavior.
Should I use OpenAI or Claude?
Use OpenAI when you want the broadest default platform for product work, structured outputs, hosted tools and multimodal tasks. Use Claude when your workload is long, agentic, code-heavy or benefits from prompt caching. Many production stacks use both: OpenAI for broad routing and Claude for long tool-heavy workflows.
Is OpenRouter good for production?
OpenRouter is useful for experimentation, fast model switching and fallback routing. For production, check the underlying provider, data policy, uptime expectations and billing behavior for each model route. It is best treated as a gateway layer, not a substitute for understanding provider-level reliability.
Is NVIDIA NIM an inference API?
Yes, but it is not priced like a normal public token API. NVIDIA NIM offers hosted prototype endpoints and downloadable NIM containers, with production tied to NVIDIA AI Enterprise licensing and GPU infrastructure. It is strongest when you want a path from free prototyping to controlled self-hosted inference.
What is the best inference API for enterprise?
AWS Bedrock is often the easiest enterprise procurement layer because it fits into AWS IAM, logging, regions, guardrails and billing. Direct OpenAI, Anthropic or Google APIs may be better for product quality, but Bedrock can be easier to approve in regulated or AWS-native organizations.
How should I route between inference APIs?
Use a frontier model for hard reasoning and premium user-facing output, a cheaper open model for classification or routine text, a batch route for offline jobs, and at least one fallback provider for outages. Log cost per successful task, not just tokens per request.
Bibliography & further reading
- OpenAI – API pricing. openai.com/api/pricing
- Anthropic – Claude pricing. anthropic.com/pricing
- Google AI for Developers – Gemini API pricing. ai.google.dev/gemini-api/docs/pricing
- Groq – Pricing. groq.com/pricing
- Together AI – Pricing. together.ai/pricing
- Fireworks AI – Pricing. fireworks.ai/pricing
- OpenRouter – Model and pricing directory. openrouter.ai/models
- NVIDIA – NIM General FAQ. docs.api.nvidia.com/nim/docs/product
- NVIDIA – NIM Microservices. nvidia.com/en-us/ai-data-science/products/nim-microservices
- AWS – Amazon Bedrock pricing. aws.amazon.com/bedrock/pricing
- Baseten – Pricing. baseten.co/pricing
- DecodeTheFuture – NVIDIA NIM API Pricing: 7 Limits to Know in 2026. decodethefuture.org/en/nvidia-nim-api-pricing-limits-guide
- DecodeTheFuture – AI Architecture for Production. decodethefuture.org/en/ai-architecture-for-production