When the first line of a pricing sheet catches your eye, it’s usually the split between what you pay to feed the model and what you pay to get it back. Mistral’s entry‑level nemo model asks $0.019 for input and $0.03 for output per 1 M tokens, Meta’s smallest Llama‑3.2‑1b‑instruct charges $0.027 for input and $0.201 for output, while Alibaba’s Qwen‑3.7‑flash sits in the middle with $0.03 input, $0.006 cached input and $0.13 output. Those three figures set the tone for a very different budget story depending on whether your workload is prompt‑heavy or generation‑heavy.
### Token rates side by side
The table below lines up the headline rates for the most comparable models from each vendor. Cached‑input discounts appear where they are offered; they matter only if you reuse prompts at scale.
| Vendor | Model | Input | Cached input | Output |
|---|---|---|---|---|
| Mistral | mistral‑nemo | $0.019 per 1M tokens | – | $0.03 per 1M tokens |
| Meta Llama | llama‑3.2‑1b‑instruct | $0.027 per 1M tokens | – | $0.201 per 1M tokens |
| Qwen (Alibaba) | qwen3.7‑flash | $0.03 per 1M tokens | $0.006 per 1M tokens | $0.13 per 1M tokens |
If your app mostly consumes user prompts and returns terse answers, Mistral’s sub‑cent input price makes it the clear winner. For a chat bot that spits out long paragraphs, Qwen’s $0.13 output fee is still far below Meta’s $0.201, giving it a decisive edge on generation‑heavy workloads. The cached‑input discount on Qwen only matters when you repeatedly hit the same prompt; otherwise the raw input price is already higher than Mistral’s.
### Choosing the right vendor
Start‑ups that need to ingest massive streams of logs, telemetry or user‑generated text will feel the sting of Meta’s higher input cost quickly, even though its output price is competitive for short replies. Mistral’s nemo and its larger siblings (like mistral‑small‑24b‑instruct‑2501 at $0.05 input, $0.08 output) give a smooth scaling path for teams that expect to grow token volume without exploding their bill.
Enterprises building content‑generation pipelines, code‑completion services, or multimodal apps that demand richer outputs will find Qwen’s flash tier attractive: the $0.13 output fee is roughly a third of Meta’s $0.201, and the $0.006 cached input rate can shave pennies off repeated prompt calls. If you need higher‑capacity models, Qwen also offers qwen3‑next‑80b‑a3b‑instruct at $0.09 input and $1.1 output, while Meta’s llama‑4‑maverick tops out at $0.2 input and $0.696 output. Those premium tiers illustrate the same pattern – Qwen’s output stays lower, but its input can be higher than Mistral’s mid‑range offerings.
In practice the choice narrows to three use‑cases. If you are token‑starved on ingestion, Mistral’s low‑cost input wins. If you need the cheapest generation per token, Qwen’s flash model is the sweet spot. If you prefer a balanced, well‑documented ecosystem with a familiar brand, Meta’s Llama line offers predictable pricing but at a premium on output. For most developers, the optimal path is to start on Mistral for prompt‑heavy prototypes, then migrate to Qwen when the output volume grows. Pricing verified on 2026-09-12.