AI DIRECTORY / VS
Model-selection trade-offs: capability · cost · latency
AI21 Labs vs OpenAI API
Latest verified observations from the BizOps AI ledger. Empty cells mean the metric does not apply or the ledger hasn't captured it yet — never a guess.
| Consumption metric | AI21 Labs | OpenAI API |
|---|---|---|
| Input tokens ($ / 1M) | $0.4 | $2 |
| Output tokens ($ / 1M) | $0.4 | $8 |
| Cache-read tokens ($ / 1M) | — | $0.5 |
| Max context window | — | 1,047,576 tok |
| 12-signal BizOps Score (method) | — | — |
Output : Input margin ratio
Where the bar crosses zero is parity; the further it extends toward Output, the more a generation-heavy workload costs beyond what the input rate alone suggests.
Token efficiency changes the real price
AI21 Labs bills output at 1.0× its input rate; OpenAI API at 4.0×. Chatty, generation-heavy agents feel that multiplier directly.
Tokenizer tax: the same sentence is not the same number of tokens
everywhere. Non-English text typically needs 20–30% more tokens for identical content,
so a nominally cheaper $/1M rate can cost more per delivered character in multilingual workloads.
Benchmark with your corpus, not the vendor's.
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