The cheapest model is usually the smallest one that still passes your task tests. Start with Llama 3.2 1B or 3B when memory and device limits dominate, consider Mistral 7B for its Apache 2.0 license and efficiency-oriented attention, and evaluate Qwen when its quantized checkpoints or language coverage fit your workload. Quantize the finalist, then measure memory, time to first token, decode speed and throughput on the hardware you will actually operate.
There is no permanent winner on cost per token. Context length, concurrent requests, quantization, accelerator, software stack, license obligations and electricity can change the result.
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What “cheapest” means for an open-source LLM
Inference cost is more than the model download. A useful comparison includes the hardware required to load the weights, memory consumed by the key-value (KV) cache, latency users experience, requests served concurrently, electricity, hosting, operations and how often you must replace or upgrade the system.
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A smaller checkpoint can be cheaper to run but fail your extraction, coding, multilingual or structured-output tests. A larger model can cost more per request yet require less human review or fewer retries. The right objective is therefore lowest total cost for an acceptable result, not the fewest parameters in isolation.
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- Quality: accuracy on representative prompts, including malformed inputs and edge cases.
- Memory: quantized weights plus runtime overhead and KV-cache growth at your target context length.
- Latency: time to first token and decode tokens per second.
- Throughput: completed tokens or requests per second at your expected concurrency.
- Compliance: license, redistribution, commercial-use and acceptable-use requirements for the exact checkpoint.
- Operations: electricity, hosting, monitoring, failover and upgrade costs.
Because these variables interact, a benchmark from another device or software stack is evidence, not a promise. Use published measurements to narrow the field, then run a small evaluation with your own prompts.
Shortlist: three practical cost-saving choices
| Model family | Published size and context | Efficiency features | License and deployment note | Best starting point |
|---|---|---|---|---|
| Llama 3.2 1B and 3B | 1.23B and 3.21B parameters; 128k context | Grouped-query attention (GQA); quantized variants | Custom Llama 3.2 Community License; review its terms for your use | Devices and services where memory limits dominate |
| Mistral 7B | 7 billion parameters; 8,192-token context in the reported architecture | GQA with eight key/value heads; sliding-window attention (SWA) | Released under Apache 2.0 | A permissive-license baseline with efficiency-focused attention |
| Qwen checkpoints | Varies by checkpoint; the repository documents multiple sizes and variants | Int4 and Int8 checkpoints; KV-cache quantization can reduce memory for longer generation | Source code is Apache 2.0, but model weights have model-specific agreements and commercial conditions | Workloads that match a particular Qwen checkpoint’s language or task coverage |
The table is a screening tool, not a ranking. Qwen’s family does not have one parameter count or one license, so identify the exact checkpoint before estimating capacity or legal obligations.
Llama 3.2 1B and 3B: the memory-first options
Why they can reduce serving cost
Meta describes Llama 3.2’s 1B and 3B text models as multilingual models for dialogue, retrieval, summarization and on-device use. Both use GQA “for improved inference scalability,” and the text models provide a 128k context length. The named sizes are 1.23 billion parameters for the 1B version and 3.21 billion for the 3B version.
The small parameter counts make these models natural first candidates for local inference, edge devices and low-concurrency services. A long context window does not make every request cheap: the KV cache still grows with the tokens retained, and concurrent long prompts can become the dominant memory cost. Treat 128k as a capability, not a target you must enable for every request.
What the device measurements show
In Meta’s Android OnePlus 12 ExecuTorch evaluation, the 1B model decoded at 19.2 tokens per second in BF16 and 50.2 tokens per second with SpinQuant. That is a 2.6× increase; the SpinQuant file was 54.1% smaller and resident-set memory (RSS) was 39.7% lower. For 3B, BF16 reached 7.6 tokens per second versus 19.7 with SpinQuant, also 2.6×; the model file was 60.3% smaller and RSS was 49.8% lower.
Those figures are device-specific measurements, not universal guarantees. They demonstrate why quantization must be measured on the target device: the speedup and memory reduction depend on the runtime, kernels and hardware as well as the model.
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When to choose 1B versus 3B
- Start with 1B when the application is tightly memory-constrained, latency-sensitive or running on a phone, laptop or embedded device.
- Try 3B when 1B misses quality tests and you have enough memory for its larger weights and KV cache.
- Keep the same evaluation set for both sizes. A faster model that produces unusable JSON or requires frequent retries is not cheaper in production.
The custom Community License is part of the engineering decision. Confirm that its terms fit your distribution, geography, product and acceptable-use requirements before deployment.
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Mistral 7B: an efficient Apache 2.0 baseline
Architecture relevant to inference
The Mistral 7B paper reports a seven-billion-parameter model with eight key/value heads and an 8,192-token context in its architecture table. GQA reduces decoding memory and accelerates inference, while SWA limits the attention work associated with long sequences. The authors reported that Mistral 7B outperformed Llama 2 13B on the benchmarks they evaluated in 2023; those are historical comparisons, not a current leaderboard.
Mistral’s appeal for cost-sensitive deployments is the combination of a still-manageable model size and an Apache 2.0 release. Apache 2.0 is often simpler for commercial redistribution than a custom community license, but you still need to preserve required notices and verify the terms of the exact artifacts you ship.
Where 7B makes sense
Use Mistral 7B as a quality-oriented baseline when a 1B or 3B model fails your representative tasks and you can support the additional weight and cache memory. Its 8,192-token reported context is materially different from Llama 3.2’s 128k capability; if your application depends on very long prompts, benchmark truncation, retrieval strategy and cache behavior rather than assuming the larger number is automatically better.
Do not infer present-day superiority from the 2023 paper. Compare current checkpoints, runtimes and quantization formats on your own workload.
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Quantization and cache options
Qwen’s official repository documents Int4 and Int8 quantized models. It also notes that KV-cache quantization can save memory during longer generation. That can increase the number of simultaneous requests a fixed accelerator supports, but the practical trade-off—quality, speed and compatibility—must be measured with the runtime you plan to use.
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License diligence is mandatory
The repository’s source code is Apache 2.0, while model weights carry model-specific license agreements and commercial-use conditions. The repository explicitly advises users to check the license agreement accompanying each model for commercial use. Record the exact model identifier, revision, quantization format and license text in your deployment inventory; “Qwen” alone is not enough to establish your rights.
When Qwen is a strong candidate
- When a particular checkpoint covers the languages, scripts or task format your evaluation requires.
- When Int4 or Int8 weights fit your memory budget without unacceptable quality loss.
- When KV-cache quantization materially improves concurrency for your context and generation lengths.
Do not compare a Qwen Int4 checkpoint with an unquantized model and attribute every speed or memory difference to architecture. Record precision, batch size, context, runtime and accelerator for every run.
How to select the lowest-cost model that still works
1. Define the production envelope
- Write down the maximum prompt and output lengths, not just an average.
- Set an expected concurrency range and a peak scenario.
- Specify the latency target separately for first-token delay and sustained decoding.
- List required languages, tools, JSON schemas, refusal behavior and citation or retrieval formats.
- Identify whether you need local, private-cloud or hosted execution and what redistribution is allowed.
These constraints prevent an attractive single-user benchmark from deciding a multi-user service.
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2. Build a representative evaluation set
Use real, de-identified prompts where possible. Include short and long contexts, normal and adversarial inputs, structured-output cases, multilingual examples and failures that previously required human correction. Score factual correctness, schema validity, task completion and refusal behavior. Keep a small fixed set for rapid iteration and a larger holdout set for the final choice.
3. Test the smallest candidates first
Run Llama 3.2 1B and 3B first when memory is the primary constraint. Add Mistral 7B when quality requires a larger model or Apache 2.0 matters. Add the Qwen checkpoints that match your languages or task, checking each license. Stop expanding the shortlist once a model clearly fails a non-negotiable requirement.
4. Quantize each finalist
Compare the precision supplied by the checkpoint with an available lower-bit variant. Record model-file size, peak process memory and output quality. Quantization can reduce memory and improve speed, but degradation is task-specific; a small loss on open-ended prose may be unacceptable for exact extraction or code.
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5. Measure on the target stack
Use the same prompt set, context lengths, batch sizes, sampling settings and runtime for every candidate. Warm up the runtime before timing. Report:
- Time to first token, including prompt processing.
- Decode tokens per second after generation begins.
- End-to-end latency for complete responses.
- Peak memory with weights and KV cache included.
- Throughput and error rate at each planned concurrency level.
- Quality scores and the number of retries or post-processing failures.
6. Convert results into a cost model
For a hosted accelerator, combine the hourly price with measured throughput and utilization. For local hardware, include purchase or lease cost, electricity, cooling, storage, maintenance and the period over which you amortize the system. Add engineering and monitoring time when one model requires more operational work. Divide total monthly cost by successfully completed tasks, not raw generated tokens, if quality failures trigger retries or human review.
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Weights are only the starting point
Parameter count and quantized file size estimate the weight footprint, but the process also needs runtime buffers and a KV cache. Longer prompts and outputs retain more attention state. Multiple simultaneous requests multiply that cache demand. A model that fits in a single-request test can fail under production concurrency.
Use context deliberately
Do not send a 128k limit to every Llama 3.2 request simply because the model supports it. Trim irrelevant history, retrieve only necessary passages and set an application-level maximum that matches your quality and latency needs. For Mistral 7B, the paper’s reported 8,192-token architecture context is a separate constraint that must be reflected in prompt construction and testing.
Quantized KV cache for long generation
Qwen’s repository identifies KV-cache quantization as a way to save memory for longer generation. Test it at the context lengths and concurrency you actually need. The relevant result is not only whether one request fits, but how many useful requests the system serves before latency or memory becomes unacceptable.
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- Identify the artifact: model family, exact checkpoint, revision and quantization.
- Read the applicable license: Llama 3.2 Community License, Apache 2.0 for Mistral 7B, or the specific Qwen model agreement.
- Check commercial use and redistribution: especially when bundling weights into a product or offering an API.
- Preserve notices and attribution: follow the obligations attached to the weights and accompanying code.
- Review acceptable-use terms: a permissive code license does not automatically grant unrestricted use of model outputs or weights.
- Record changes: a new checkpoint or quantization can have different terms even within one family.
For Qwen, do not rely on the repository’s Apache 2.0 source-code license as a substitute for the model-weight agreement. For Llama 3.2, treat the custom community license as a deployment requirement, not a footnote.
Common mistakes that make inference look cheaper than it is
Using someone else’s tokens-per-second number
Hardware, drivers, kernels, batch size, context, precision and sampling all affect speed. Meta’s OnePlus 12 figures illustrate the point: SpinQuant changed both file size and RSS as well as decode speed, but those measurements do not predict every phone, GPU or CPU.
Optimizing weight memory while ignoring the cache
Short prompts can hide the memory consumed by long conversations and concurrent users. Include worst-case context and output lengths in peak-memory tests.
Comparing quality with different prompts
A larger model can appear better simply because it received more context or a more carefully engineered instruction. Keep inputs, decoding settings and post-processing rules consistent.
Treating a benchmark win as a product decision
The Mistral 7B paper’s comparison with Llama 2 13B is historical evidence from 2023. It does not establish a universal 2026 ranking, nor does it measure your cost per successful task.
Choosing a checkpoint before reading its license
Model family names conceal different terms, sizes and capabilities. Store license evidence with the deployment configuration so an upgrade does not silently change your rights.
Practical recommendations by deployment constraint
| Your constraint | First model to test | What to verify before committing |
|---|---|---|
| Phone, edge device or very limited RAM | Llama 3.2 1B | Quantized quality, sustained thermals, RSS under realistic context and the Community License |
| Small device where 1B quality is insufficient | Llama 3.2 3B | Peak memory, latency after warm-up and concurrency at your chosen context |
| Need a larger baseline with a permissive code-and-model release | Mistral 7B | Apache 2.0 obligations, 8,192-token context behavior and quantized quality |
| Language or task coverage points to a Qwen checkpoint | That exact Qwen checkpoint | Model-weight license, Int4/Int8 quality, KV-cache behavior and runtime support |
| High concurrency with long generations | Whichever finalist has the best measured cache and throughput profile | Peak memory, queueing latency, error rate and cost per successful response |
A simple decision rule
- Reject any model that fails a mandatory quality, safety or licensing requirement.
- Among the survivors, choose the smallest model whose quality score clears your threshold.
- Quantize it and repeat the quality and systems tests.
- Choose the configuration with the lowest measured total cost per successful task at production concurrency.
- Keep the next-best model evaluated as a fallback for quality regressions, hardware changes or license changes.
In most memory-constrained deployments, that process leads you to test Llama 3.2 1B or 3B first. Mistral 7B is a sensible next step when quality or Apache 2.0 licensing outweighs the extra compute. Qwen can be the best answer when a specific checkpoint’s coverage and quantized deployment profile fit your workload, provided its exact weight license is acceptable.
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