If your goal is benchmarking throughput and latency on small workloads, the “best” free LLM gateway providers are usually the ones that offer:

• OpenAI-compatible API
• No-credit-card free tier or generous trial
• Low throttling / predictable rate limits
• Fast routing to multiple model providers
• Easy logging of latency and token usage

Here are the strongest options to look at:

1) OpenRouter

Best overall for benchmarking flexibility

• Pros
  • OpenAI-compatible API
  • Huge model catalog across providers
  • Easy to compare multiple models through one gateway
  • Good for latency/throughput comparisons because you can keep client code constant
• Cons
  • Free usage is limited and availability can vary
  • Latency depends on the upstream model/provider
• Best for
  • Comparing many models with the same harness
  • Small-scale latency tests and routing experiments

2) Groq API

Best for raw low latency

• Pros
  • Extremely fast inference for supported models
  • OpenAI-compatible style API
  • Very useful for measuring “best-case” throughput/latency
• Cons
  • Limited model set
  • Free access may be constrained by quota/rate limits
• Best for
  • Measuring lowest possible latency on small prompts
  • Testing throughput under fast-response conditions

3) Together.ai

Best for broad model access with a usable free tier

• Pros
  • Wide variety of open models
  • Developer-friendly API
  • Often usable for small benchmarks without much setup
• Cons
  • Free tier/rate limits may change
  • Latency varies a lot by model
• Best for
  • Comparing open-weight models
  • Small throughput tests across different architectures

4) Fireworks.ai

Good for benchmarking open models with decent performance

• Pros
  • OpenAI-compatible APIs for many models
  • Good performance on popular open models
  • Simple integration
• Cons
  • Free allowance may be limited
  • Model availability and performance vary
• Best for
  • Small benchmark runs on instruction-tuned/open models

5) DeepInfra

Good low-cost/free-tier option for open models

• Pros
  • OpenAI-compatible endpoints
  • Supports many open models
  • Straightforward to use for benchmark scripts
• Cons
  • Free access can be limited
  • Latency is model-dependent
• Best for
  • Small workload latency/throughput measurement
  • Evaluating different open models quickly

6) Hugging Face Inference API / Providers

Good for experimentation, not always ideal for strict benchmarking

• Pros
  • Easy access to many models
  • Sometimes includes free/community access
  • Good for quick tests
• Cons
  • Latency can be inconsistent
  • Free access may be rate-limited or queued
• Best for
  • Quick smoke tests
  • Lightweight benchmarking, not precision latency studies

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My practical recommendation

If you want meaningful small-workload benchmarking, use:

For gateway comparison:

• OpenRouter
  Best when you want one client to test many providers/models.

For low-latency ceiling tests:

• Groq
  Best to see how fast a highly optimized inference stack can be.

For open-model comparisons:

• Together.ai or Fireworks.ai
  Good balance of access and performance.

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What to watch out for when benchmarking free tiers

Free gateways often distort results because of:

• Cold starts
• Queueing
• Shared capacity
• Bursty rate limits
• Automatic model fallback/routing
• Different context/window/tokenization behavior

So for small workloads:

• Run multiple warm-up requests
• Measure p50 and p95 latency
• Test both:
  • single-request latency
  • parallel request throughput
• Keep prompts short and consistent
• Use the same max output tokens across providers

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Simple shortlist

If I had to choose only three:

1. OpenRouter — best general benchmark gateway
2. Groq — best low-latency benchmark target
3. Together.ai — best for open-model variety

If you want, I can also give you:

• a comparison table
• a ready-to-run benchmark script
• or a list of providers with free-tier limits and OpenAI compatibility