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For most buyers, NanoGPT is the best starting point if you want access to many models through one OpenAI-compatible API. OpenAI and Anthropic are better fits if you’re choosing within their own ecosystems. The price caveat: a listed starting price doesn’t tell you what production usage will cost, and several providers don’t publish rates. We ranked all 12 by breadth, fit and price clarity, not by score alone. Check model-level costs, usage limits and deployment requirements before switching or building around an API.
Quick picksThe shortlist
NanoGPTNanoGPTBest overall · $12/mo
OpenAIOpenAIBest for ChatGPT and API · $8/mo
AnthropicAnthropicBest for Claude teams · $17/mo
CohereCohereBest for enterprise search · Free
MiniMaxMiniMaxBest for speech APIs · $4/mo
Gemini Live APIGemini Live APIBest for live voice · Free
Amazon BedrockAmazon BedrockBest for AWS teams · Custom
Azure OpenAI ServiceAzure OpenAI ServiceBest for Azure teams · Custom
Ask SageAsk SageBest for secure deployments · $30/mo
ZenMuxZenMuxBest for provider routing · Custom
Upstage AIUpstage AIBest for document workflows · $100/mo
01Compared at a glance
| Tool | Score | From | Free plan | Trial |
|---|---|---|---|---|
| NanoGPTNanoGPT | 7.1 | $12/mo | Yes | — |
| OpenAIOpenAI | 6.2 | $8/mo | Yes | — |
| AnthropicAnthropic | 6.8 | $17/mo | Yes | — |
| CohereCohere | 6.8 | Free | Yes | — |
| MiniMaxMiniMax | 6.3 | $4/mo | No | — |
| Gemini Live APIGemini Live API | 6.8 | Free | Yes | — |
| Amazon BedrockAmazon Bedrock | 5.5 | Custom | No | — |
| Azure OpenAI ServiceAzure OpenAI Service | 6.0 | Custom | No | 30 days |
| Ask SageAsk Sage | 6.5 | $30/mo | No | 30 days |
| ZenMuxZenMux | 6.5 | Custom | No | — |
| Upstage AIUpstage AI | 6.3 | $100/mo | No | — |
02The picks, in detail
- Best overallNanoGPTNanoGPT
NanoGPT is our best starting point for buyers who want one API-compatible service across many models. The pricing page puts it at $12 with a free plan and highlights usage-based costs. That breadth is useful, but it does not make every model equally good value: compare each model’s rate and be cautious about subscription routing, which the listing says can be uneven. It suits teams willing to track spend rather than assume one flat price.
What works
- Lists 1,277 models, including ChatGPT, Claude, and Gemini
- Pay-as-you-go access avoids a required monthly plan
- Pro includes 60M input tokens per week
What doesn't
- Pro costs $12/month, with no annual option published
- Paid models outside Pro cost extra at list prices
- Manually pinning a provider adds 5%
- Best for ChatGPT and APIOpenAIOpenAI
OpenAI is a practical pick for buyers who want ChatGPT alongside API access, or teams that need a shared AI workspace. The pricing page gives a starting price of $8 and notes a free plan. That entry point doesn’t settle the cost question: check plan caps and API usage costs before committing. We rank it near the top for its combination of familiar product options, but not as a guaranteed low-cost API.
What works
- Free ChatGPT plan with limited daily quotas
- Go starts at $8 per month
- ChatGPT, Business and API Platform are listed offerings
What doesn't
- Free plan has limited daily quotas across listed tools
- Plus lists periodic five-hour rolling usage caps
- Go may include ads
- Best for Claude teamsAnthropicAnthropic
Anthropic makes sense for teams already choosing Claude or looking for centralized billing across a team. The pricing page puts the starting price at $17 and notes a free plan, while also flagging model tiers and usage-based API costs. Those details make it difficult to compare a headline starting price with likely production spend. Review the applicable plan limits and model rates before standardizing on Claude.
What works
- Free plan includes web, desktop and mobile access
- Free plan includes up to five projects
- Pro includes Claude Code and Projects
What doesn't
- Free and Pro usage is capped over five-hour and weekly periods
- Annual Pro billing requires $200 upfront
- API token rates vary by model and input or output
- Best for enterprise searchCohereCohere
Cohere is a fit for businesses combining search with AI agents, or enterprise teams that need private deployment or hosted inference. The pricing page notes a free plan, but does not give a paid starting price; production pricing is described as opaque. That makes cost comparisons difficult, especially before usage is clear. Consider it for the enterprise requirements it addresses, but get pricing for your deployment and workload before switching.
What works
- Free, rate-limited API calls for prototyping
- On-premises and isolated VPC deployment options
- Models cover generation, search, speech, translation, and parsing
What doesn't
- Trial API calls aren't allowed for production or commercial use
- Current production API token rates aren't published
- North and Compass require custom enterprise pricing
- Best for speech APIsMiniMaxMiniMax
MiniMax is worth considering for speech workloads, multimodal model exploration and buyers looking for an open-weight model. Its published starting price is $4, but the listing says text API pricing is unclear and video plans can be costly. That low entry figure is not enough to judge total API value. Confirm rates for the specific models and modalities you plan to use before building your budget around it.
What works
- Broad text and multimodal model lineup
- H3 is listed as open-weight
- Audio plans support all speech models
What doesn't
- Listed plans don't show text-model API pricing
- Video Standard costs $1,000 per month
- Video packages are valid for one month
- Best for live voiceGemini Live APIGemini Live API
Gemini Live API is aimed at developers building real-time voice agents or multimodal streaming apps, particularly in Google’s ecosystem. The pricing page notes a free plan but gives no starting price. That leaves the production case unresolved: check token costs and limits before committing to a live workload. It ranks below broader options for general API buyers because its stated fit is specific to real-time and streaming use.
What works
- Streams audio, images, video frames, and text
- Supports interruptions and voice activity detection
- Includes asynchronous and parallel function calling
What doesn't
- No fixed monthly starting price
- Free-tier quotas are not published
- Exact paid rate limits are not published
- Best for AWS teamsAmazon BedrockAmazon Bedrock
Amazon Bedrock is a sensible option for teams already running workloads on AWS, especially when inference varies or batch jobs can wait. The pricing page highlights cost controls and batch inference, but gives no starting price. It also advises verifying model access before committing. That combination makes Bedrock worth evaluating within an AWS setup, not an easy price winner: budget the workload and confirm the models you need are available.
What works
- Supports inference and agent building at scale
- Offers on-demand and batch inference modes
- Batch pricing is listed at 50% below on-demand
What doesn't
- No published monthly or annual subscription price
- Homepage omits model and limit details
- Usage quotas and rate limits apply
- Best for Azure teamsAzure OpenAI ServiceAzure OpenAI Service
Azure OpenAI Service suits teams already building on Azure, with variable-consumption API workloads or non-urgent processing that can use batch. The pricing page gives no starting price, so comparing its costs with providers that publish an entry price is difficult. Treat it as an Azure-native option to price against your actual workload, and confirm rates before you commit rather than assuming familiarity with Azure makes it cheaper.
What works
- Foundry Models supports discovery from exploration through deployment.
- Microsoft Foundry covers building and evaluating generative AI solutions.
- Microsoft Foundry supports building custom agents.
What doesn't
- Standard and Provisioned prices are not published.
- Batch results are returned within 24 hours.
- Excess Standard requests are throttled.
- Best for secure deploymentsAsk SageAsk Sage
Ask Sage is designed for government and defense teams, air-gapped or on-premise deployments, and buyers comparing multiple models. The pricing page starts at $30, but also flags token budgets and unpublished plan prices. That means the entry figure may not tell you whether a deployment fits your needs or budget. It belongs on a shortlist when secure deployment matters; verify token allowances and get full plan pricing before switching.
What works
- Access to 150+ models, including major commercial and open-source options
- Supports text, image, video and audio tasks
- Deployment options include cloud, on-premise, edge and air-gapped environments
What doesn't
- No free plan; the listed trial lasts 30 days
- Starter's $30 monthly price includes 200,000 tokens
- Professional listings disagree on the monthly token allowance
- Best for provider routingZenMuxZenMux
ZenMux consolidates access to multiple model providers behind a unified API, with routing and failover for teams that value those controls. The pricing page gives no starting price, and describes user feedback as thin, so the value case is not yet clear from the available information. It may reduce the friction of working across providers, but compare its price and routing terms with managing providers directly before adopting it.
What works
- One account and API for multiple AI models
- Compatible with OpenAI, Anthropic, and Vertex AI protocols
- Web interface supports chat, image, and video generation
What doesn't
- No published starting price or subscription plans
- Model rates appear on individual pages, not as one price
- No free plan or free trial is listed in the pricing data
- Best for document workflowsUpstage AIUpstage AI
Upstage AI is aimed at teams processing invoices and contracts, enterprises needing on-premise deployment, and agent workloads that take documents as input. The pricing page gives a $100 prepaid starting tier but says overage costs are unclear. That is a meaningful budget caveat for workloads that can grow. Consider it for document-heavy enterprise use, but get the overage terms and deployment pricing in writing before committing.
What works
- Solar Pro 4 targets tool calling, terminal tasks, and long-document reasoning.
- Solar Mini 4 has 3B active parameters for cost-sensitive agent workloads.
- Document Parse handles PDFs, scans, and emails.
What doesn't
- Explore starts at $100/month prepaid; no free plan is listed.
- Yearly commitments total $1,200 at Explore, $6,000 at Build, or $60,000 at Scale.
- Post-credit usage rates are not published on the vendor's site.
03How we chose
We weigh published pricing, stated plan limits, documentation, and Reddit sentiment when it is available. We look for costs and constraints that could change a buying decision, such as how usage is billed and what the provider says about access or quotas. We also consider whether the documentation gives buyers enough information to assess the API before committing. We do not treat missing details as proof of a limitation: when pricing or a limit is not published in the material we review, we say so. This is a source-based comparison, not a report of hands-on testing.
04Match the API to the job before comparing providers
Start with the application you need to build, then define its requirements before comparing OpenAI, MiniMax, Cohere, Amazon Bedrock, Ask Sage, Anthropic, Upstage AI, Azure OpenAI Service, NanoGPT, Gemini Live API, and ZenMux. List the tasks the model must handle, the expected request volume, and any requirements your team has for data handling, regions, latency, or integration. Treat those as questions to verify in each provider’s current documentation, not assumptions about what a product supports. Check which models and endpoints are available, what limits apply, and whether the API fits your existing infrastructure. If a requirement is not documented, mark it as unconfirmed and ask the provider before building around it. A long model list is not a substitute for a fit with your actual workload.
05Compare the full cost and the limits that shape it
Do not compare API providers on a headline rate alone. Check the current pricing page for the billing unit, any separate charges, and whether the page explains how usage is counted. Then read the documentation for rate limits, quotas, model access, and any conditions that could affect your expected traffic. These details may be unpublished or depend on an account or agreement; do not fill the gaps with estimates. Build a simple cost model using your own expected request volume and the provider’s published prices, and label any unknown inputs. Before switching or committing, confirm the terms directly with the provider. For a shortlist, compare the cost of serving the same workload, the limits that matter to it, and the effort your team expects to spend on integration and migration.
Questions people ask
How should we compare LLM API providers?
Compare providers against the same workload and requirements. Review published pricing, documented limits, model and endpoint details, and data-handling information. Mark anything the provider does not publish as unknown, then confirm important gaps before committing. Avoid choosing on a headline price or model list alone.
Which LLM API provider is cheapest?
There is no reliable answer without a workload and current published prices. Billing units, usage, and additional charges can affect the total. Compare providers using the same request assumptions, and treat missing pricing details as unknown rather than estimating them. Confirm current rates on the provider’s pricing page.
What should we check before switching LLM APIs?
Check whether the replacement documents the models, endpoints, limits, and data-handling terms your application requires. Estimate costs using your expected usage and published prices. Identify undocumented requirements and confirm them with the provider. Plan for integration changes instead of assuming one API can replace another without adjustment.
Are Reddit reviews enough to choose an LLM API?
No. Reddit posts can help surface user-reported concerns, but they are not a substitute for current pricing pages, documentation, or confirmation from the provider. Treat comments as anecdotal evidence, check whether they match your use case, and verify claims that could affect cost, access, or reliability.