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There is no permanently cheapest open-source LLM. The lowest-cost choice is the smallest model that passes your own quality tests at the context length and concurrency you need. Quantize that model, then measure peak memory, time to first token, decode speed and total operating cost on the hardware you will actually run.
For a practical starting point, evaluate Llama 3.2 1B and 3B when device memory is tight, Mistral 7B when an Apache 2.0 baseline and efficient attention matter, and Qwen when its language coverage or quantized checkpoints fit your workload. Treat all three as candidates, not as a universal ranking.
What makes an open-source LLM inexpensive to run?
Inference cost is the expense of producing tokens after a model is deployed. A useful comparison includes much more than the parameter count shown on a model page:
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- Weights: the model files must fit in accelerator memory or be partly offloaded to system RAM. Lower-bit quantization reduces this footprint.
- KV cache: attention stores key and value tensors for every active sequence. It grows with context length, batch size and concurrency, so a model that fits for one short prompt may become expensive under production load.
- Prefill and decode work: processing a long prompt affects time to first token; generating each subsequent token affects decode speed. Users experience both.
- Throughput: a server handling many simultaneous requests can be cheaper per token than a single-user process, but only if latency remains acceptable.
- Hardware and operations: include the accelerator or host, electricity, storage, monitoring, upgrades, cooling and engineering time.
- License obligations: redistribution, commercial use and acceptable-use terms can create legal or operational costs even when the weights are free.
Parameter count is therefore a useful first filter, not a final cost metric. A smaller model with poor output quality can require retries, larger prompts, human correction or a second model, erasing its apparent saving.
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At-a-glance comparison
| Model family | Sizes and context | Efficiency features | License information | Best initial use |
|---|---|---|---|---|
| Llama 3.2 text models | 1B (1.23B parameters) and 3B (3.21B); 128k context | Grouped-query attention (GQA); official quantized variants | Custom Llama 3.2 Community License | On-device or memory-constrained inference |
| Mistral 7B | 7 billion parameters; 8,192-token context in the reported architecture | GQA with eight key/value heads; sliding-window attention (SWA) | Apache 2.0 for the released model | General-purpose baseline where permissive licensing is important |
| Qwen checkpoints | Varies by checkpoint; repository documents Int4 and Int8 variants | Weight quantization and optional KV-cache quantization | Model-specific agreements and commercial conditions | Workloads matching a particular Qwen language or task checkpoint |
The table intentionally does not assign a dollar-per-token winner. The available evidence does not provide a current, apples-to-apples cost benchmark across these families, and results change with hardware, quantization, software, context and batch size.
Llama 3.2 1B and 3B: the first models to test on constrained hardware
What you get
Meta’s Llama 3.2 text models are available in 1B and 3B sizes, with 128k context, grouped-query attention and quantized variants. The model card positions them for dialogue, retrieval, summarization and on-device use. The parameter figures are approximately 1.23 billion for the 1B model and 3.21 billion for the 3B model.
GQA uses fewer key/value heads than a conventional multi-head design, reducing the memory traffic associated with decoding. That matters most when generation is long or when several requests share the accelerator. The 128k context limit is a capability, not a promise that every device can serve 128k tokens economically; KV-cache memory can become the dominant resource well before the limit.
What the published device test shows
Meta reported an Android OnePlus 12 ExecuTorch evaluation that illustrates the effect of quantization, but it is not a universal benchmark. In that test, the 1B model decoded at 19.2 tokens per second in BF16 and 50.2 tokens per second with SpinQuant, a 2.6-times increase. SpinQuant reduced the model file by 54.1% and resident-set memory (RSS) by 39.7%.
For the 3B model on the same evaluation, BF16 reached 7.6 tokens per second and SpinQuant reached 19.7 tokens per second, again about 2.6 times faster. The model file was 60.3% smaller and RSS was 49.8% lower with SpinQuant. These figures describe that phone, runtime and test configuration; they should not be converted into guaranteed desktop, server or cloud speeds.
When Llama 3.2 is the economical choice
- Use 1B when your task is narrow, prompts are short and a small quality drop is acceptable.
- Use 3B when 1B fails factuality, instruction-following or structured-output checks but a 7B-class model will not fit comfortably.
- Prefer the family when its community-license terms fit your distribution and acceptable-use requirements.
- Test the official quantized formats and your chosen runtime rather than assuming a generic 4-bit implementation will behave identically.
Small models are especially attractive for local assistants, classification, extraction and retrieval post-processing. They are less suitable when your evaluation requires deep multi-step reasoning, broad world knowledge or consistently complex tool plans; in those cases, a larger model may be cheaper overall because it avoids retries and manual review.
Mistral 7B: an efficient Apache 2.0 baseline
Architecture and reported capability
The Mistral 7B paper describes a seven-billion-parameter model with eight key/value heads, grouped-query attention and an 8,192-token context in its reported architecture table. It combines GQA for faster inference and lower decoding memory with sliding-window attention to reduce the cost of long sequences. The paper says Mistral 7B outperformed Llama 2 13B on the benchmarks it evaluated; those comparisons date from 2023 and are historical evidence, not a current leaderboard.
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Sliding-window attention limits how much of a long sequence each layer must attend to. It can reduce work and memory pressure, but it also changes how information from distant tokens is handled. Validate retrieval, summarization and long-document tasks with your own prompts instead of assuming that a lower attention cost preserves every capability.
Why the license can change the economics
Mistral 7B was released under Apache 2.0, a permissive license commonly used for commercial software and redistribution. You still need to preserve required notices and check the specific release you download, but the licensing review is generally more straightforward than for a model with a custom community agreement. A permissive license can lower legal friction when you plan to ship weights, bundle them in a product or offer a hosted service.
When to choose it
- Choose Mistral 7B as a strong baseline when a 1B or 3B model does not meet quality targets.
- Use it when GQA and sliding-window attention are useful for your latency or long-sequence profile.
- Consider it for teams that value a clear Apache 2.0 starting point, subject to verification of the exact artifact and any surrounding components.
A 7B model is not automatically more expensive than a smaller model. If it completes a task in one pass while a 3B model needs several attempts, its cost per successful result can be lower. Measure successful outputs, not only raw generated tokens.
Qwen: compare the exact checkpoint, not just the family name
Quantization and cache options
Qwen’s official repository documents Int4 and Int8 quantized models and explains that KV-cache quantization can save memory during longer generation. Weight quantization reduces the storage and memory required for the model itself; KV-cache quantization targets the per-request memory that grows with context and concurrency. Those are different levers, and a deployment may need both.
Quantization can affect quality, calibration requirements and kernel support. An Int4 checkpoint that runs efficiently on one accelerator may be slower on another if the runtime lacks optimized kernels. Evaluate prompt processing and token generation separately, because a format can improve one phase while leaving the other unchanged.
License checks are mandatory
The repository source code is Apache 2.0, but Qwen states that model weights have model-specific license agreements and commercial-use conditions. Do not infer the weights’ permissions from the repository’s code license. Before deployment, record the exact checkpoint name and revision, read its accompanying license, verify commercial and redistribution rights, and check any acceptable-use restrictions.
When Qwen is a good fit
- Choose a Qwen checkpoint when its tested languages, instruction behavior or structured-output performance match your application.
- Prefer it when the available Int4, Int8 or KV-cache options map well to your target hardware and context length.
- Reject it, or select another checkpoint, if the license cannot support your intended product or geography.
How quantization changes cost
Quantization represents weights with fewer bits than BF16 or FP16. In broad terms, fewer bits mean smaller files, lower weight memory and often less data movement during decoding. The actual speedup depends on the accelerator, kernel implementation, batch size and runtime.
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Weight quantization versus KV-cache quantization
- Weight quantization is mostly a fixed saving per loaded model. It helps every request and can make a model fit on a smaller device.
- KV-cache quantization is a scaling saving. It becomes increasingly valuable as prompts get longer or more users generate concurrently.
- Activation and workspace memory remain. A model that appears to fit based only on its quantized file can still run out of memory during prefill or batching.
Start with the lowest-bit format that passes your quality tests, then verify that the runtime supports it efficiently. If quality falls below the acceptance threshold, move up to a higher-bit format or a larger model and recalculate the cost per successful answer.
A repeatable selection procedure
- Define the workload. Record prompt length distribution, expected output length, peak simultaneous requests, latency target, uptime and whether requests are interactive or batch.
- Create a representative evaluation set. Include real user prompts, difficult edge cases, required refusal behavior, retrieval questions and structured-output examples. Keep a held-out set for the final decision.
- Set quality gates before benchmarking speed. Specify pass rates for factuality, schema validity, language coverage, safety and human review. A fast model that fails a gate is not a cost-saving option.
- Shortlist by memory. Test Llama 3.2 1B and 3B, Mistral 7B and relevant Qwen checkpoints in formats your target runtime supports. Include model weights, runtime overhead, KV cache and safety margin in the memory estimate.
- Benchmark the actual accelerator. Warm up the runtime, then measure time to first token, prompt-processing rate, decode tokens per second, peak memory and error rate at your intended context lengths and concurrency levels.
- Repeat with quantized formats. Compare BF16 or FP16 with the supported Int8, Int4 or other formats. Record quality changes and whether the runtime falls back to slower kernels.
- Calculate total cost. Multiply host or accelerator cost, electricity and operations by the hours required, then divide by useful or successful tokens. Add storage, network, monitoring and expected upgrade costs.
- Run a production-shaped pilot. Exercise retries, cancellations, queueing, model loading, autoscaling and long-context requests. Keep the winner only if it meets both quality and service-level targets.
What to measure in a fair benchmark
| Metric | Why it matters | How to report it |
|---|---|---|
| Quality pass rate | Prevents a cheap but unusable model from winning | Report the task set, scoring rule and held-out results |
| Time to first token | Determines perceived responsiveness and prompt-processing cost | Report median and tail latency at each context length |
| Decode speed | Controls waiting time for generated output | Report tokens per second after warm-up |
| Peak memory | Determines whether the deployment fits and how much concurrency is possible | Include weights, KV cache, workspace and runtime overhead |
| Throughput | Shows cost efficiency under concurrent load | State batch size, concurrency, context and output lengths |
| Failure and retry rate | Captures hidden work and user-visible errors | Count out-of-memory events, timeouts, invalid schemas and retries |
Keep software versions, drivers, tokenizer settings and sampling parameters fixed while comparing candidates. A benchmark without those conditions is difficult to reproduce and can produce a misleading ranking.
Estimating memory and concurrency
Separate fixed and variable memory in your deployment plan. Fixed memory includes quantized weights, tokenizer data and runtime buffers. Variable memory includes KV cache, temporary activations and batching workspaces. Context length and active sequence count primarily drive the variable portion.
Plan for the longest realistic prompt, not the advertised maximum. A 128k-capable model can be economical for short requests yet impractical when users routinely approach that limit. If long documents are occasional, consider truncation, retrieval or a separate long-context route rather than reserving maximum KV-cache capacity for every request.
Concurrency also changes the answer. One request may fit comfortably, while ten simultaneous generations exhaust memory or force CPU offload. Test at the service’s peak concurrency and include a headroom margin for fragmentation and transient allocations.
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Use a transparent equation rather than a headline price:
cost per successful request = (hardware or hosting + electricity + storage + networking + operations) / successful requests
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For token accounting, track prompt and completion tokens separately. Prefill-heavy applications may benefit from a model with faster prompt processing, while chat applications with short prompts and long answers may be dominated by decode speed. Include failed requests, retries and human escalation in the denominator; otherwise the result describes raw generation, not delivered value.
Run at least three scenarios: normal traffic, peak concurrency and a long-context case. A model that wins at one request can lose at peak load because of KV-cache growth or queueing. Recalculate when hardware prices, electricity rates, model versions or traffic mix change.
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- Identify the exact model checkpoint, revision and quantized artifact.
- Read the license attached to the weights, not only the runtime or repository code.
- Confirm commercial use, redistribution, modification and hosted-service permissions for your jurisdiction.
- Record required notices and attribution in your product documentation.
- Review acceptable-use and safety requirements, especially for user-generated content.
- Check whether a quantized derivative has additional terms or a separate distribution license.
- Keep an internal copy of the license and model card used for the release you shipped.
Llama 3.2 uses a custom Llama 3.2 Community License. Mistral 7B is described as Apache 2.0. Qwen’s repository warns that each model’s weights can have their own agreement and commercial conditions. These differences can outweigh a small hardware saving when you distribute a product at scale.
Common mistakes and fixes
Choosing by parameter count alone
Symptom: the smallest model appears cheapest but produces invalid JSON, misses required facts or needs repeated retries. Fix: make quality pass rate and cost per successful result part of the selection gate.
Using a benchmark from another device
Symptom: published tokens-per-second numbers do not match your server or phone. Fix: treat figures such as Meta’s OnePlus 12 results as device-specific examples and benchmark your own accelerator, runtime and quantization.
Ignoring KV-cache growth
Symptom: a model fits for one short prompt but crashes or slows under long conversations. Fix: test realistic context lengths and concurrency, and evaluate KV-cache quantization where supported.
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Symptom: deployment approval stalls after engineering work is complete. Fix: review the exact weight license before selecting the checkpoint and document the decision.
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Comparing formats with different settings
Symptom: one model receives a shorter prompt, smaller batch or different sampling parameters. Fix: hold tokenizer, prompts, generation limits, batch size and software stack constant, then repeat tests across all formats.
Decision guide
Choose Llama 3.2 1B
Select the 1B model when memory, battery or edge-device limits dominate and your evaluation accepts its quality. Quantized variants are especially worth testing for local inference.
Choose Llama 3.2 3B
Select 3B when 1B misses your quality gate but you still need a compact model and 128k context support. Verify that the community-license terms fit your use.
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Select Mistral 7B when a seven-billion-parameter baseline, GQA, sliding-window attention and an Apache 2.0 release are valuable. Validate long-sequence behavior rather than relying on historical benchmark claims.
Choose a Qwen checkpoint
Select Qwen when a specific checkpoint wins on your languages or tasks and its Int4, Int8 or KV-cache options fit your runtime. Complete the model-specific license review before production.
Choose none of these yet
If every candidate fails quality, latency or licensing requirements, keep the evaluation open. A proprietary API, a larger open model or a specialized model may have a lower total cost for your actual workload. The correct comparison is the cost of a successful, compliant result, not the nominal price of model files.
Bottom line
Start with Llama 3.2 1B or 3B for constrained devices, Mistral 7B for an efficient Apache 2.0 baseline, and the exact Qwen checkpoint that matches your task and license needs. Quantize only after establishing a quality baseline, then measure memory, time to first token, decode speed, throughput and failure rate at production context and concurrency. Revisit the decision whenever traffic, hardware, software or model licenses change.
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