LLM Cost & Capability Benchmark (2026)
An open, vendor-neutral snapshot of the 2026 frontier LLM landscape. Pricing is per 1M tokens at publicly listed rates. Quality tier reflects observed performance on standard reasoning and instruction-following evals; treat as a starting point, not a verdict for your workload.
The table
| Model | Vendor | $/1M in | $/1M out | Context | Tier |
|---|---|---|---|---|---|
| Gemini 2.5 Flash | $0.07 | $0.30 | 1,000K | cheap | |
| GPT-4o mini | OpenAI | $0.15 | $0.60 | 128K | cheap |
| Llama 3.3 70B | Meta (via Bedrock / Together / Groq) | $0.72 | $0.72 | 128K | cheap |
| GPT-5 mini | OpenAI | $0.25 | $2.00 | 400K | balanced |
| Amazon Nova Pro | AWS Bedrock | $0.80 | $3.20 | 300K | balanced |
| Claude Haiku 4.5 | Anthropic | $1.00 | $5.00 | 200K | balanced |
| GPT-5 | OpenAI | $1.25 | $10.00 | 400K | premium |
| Gemini 2.5 Pro | $1.25 | $10.00 | 2,000K | premium | |
| Claude Sonnet 4.5 | Anthropic | $3.00 | $15.00 | 200K | premium |
| Claude Opus 4.7 | Anthropic | $15.00 | $75.00 | 200K | premium |
Last updated 2026-05-27. Sorted by combined per-1M cost.
How we publish benchmarks
This page is the public face of the same model catalog Inference Labs uses internally for routing. We do not run synthetic benchmarks; instead we publish the per-token cost surface (which is objective and verifiable from each provider's docs) and use real production traces -- redacted, opt-in -- to compute quality tiers over time.
For a workload-specific benchmark, point Inference Labs at a real eval pack and it will run every model in your allowlist against the same prompts, returning a per-model pass-rate, average latency, and cost. See Data Refiner for the pipeline.
License
This table is published under CC BY 4.0. Use it however you want; a link back to inference-labs.com/benchmarks is appreciated.
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