AI Rate Cardqwen2-72b-instruct vs LLaMa_V2-13B-Chat-HF: API price and specs
LLaMa_V2-13B-Chat-HF is 63% cheaper than qwen2-72b-instruct on input price.
LLaMa_V2-13B-Chat-HF is 68% cheaper than qwen2-72b-instruct on output price.
LLaMa_V2-13B-Chat-HF is 64% cheaper than qwen2-72b-instruct on a 1M-in / 200k-out workload.
qwen2-72b-instruct has a 700% larger context than LLaMa_V2-13B-Chat-HF.
| Field | qwen2-72b-instruct | LLaMa_V2-13B-Chat-HF |
|---|---|---|
| Lowest input, per 1M | $0.35 +169.2% | $0.13 |
| Median input, per 1M | $0.80 | $0.81 +1.9% |
| Lowest output, per 1M | $0.40 +207.7% | $0.13 |
| Median output, per 1M | $0.80 | $1.11 +39.4% |
| Lowest unit price | — | — |
| Cost of 1M in + 200k out | $0.43 +175.6% | $0.16 |
| Context | 33K | 4K +87.5% |
| Max output | — | 4K |
| Providers | 3 | 2 +33.3% |
| Regions | — | — |
| Accepts | — | — |
| Returns | text | — |
| Type | Text models | Text models |
| Free option | no | no |
| Cheapest provider | deepinfra | deepinfra |
Your own workload: how many tokens the model gets (input) and gives back (output) - for example, in a month. The "Cost" row of the table above is recalculated for these numbers, for each model.
Computed from current offers each time the page opens; the best value of a row is bold, the percent is how far behind the best.
Providers selling both
Frequently asked questions
Which is cheaper, qwen2-72b-instruct or LLaMa_V2-13B-Chat-HF?
LLaMa_V2-13B-Chat-HF is 63% cheaper than qwen2-72b-instruct on input price. LLaMa_V2-13B-Chat-HF is 68% cheaper than qwen2-72b-instruct on output price. LLaMa_V2-13B-Chat-HF is 64% cheaper than qwen2-72b-instruct on a 1M-in / 200k-out workload.
Which has the larger context, qwen2-72b-instruct or LLaMa_V2-13B-Chat-HF?
qwen2-72b-instruct: 33K tokens; LLaMa_V2-13B-Chat-HF: 4K tokens.
Where can I use both qwen2-72b-instruct and LLaMa_V2-13B-Chat-HF?
deepinfra, featherless