The cheapest AI model with API access on the InferenceBench Leaderboard right now costs $0.027 per million tokens.
That is not a typo. For context, GPT-4o costs $2.50 per million input tokens and $10.00 per million output tokens. The cheapest model on InferenceBench is approximately 92x cheaper on input and 370x cheaper on output than GPT-4o.
The real question is not which model is cheapest. It is whether cheap is good enough for your workload. Here is what the data actually shows.
Quick Answer
According to the InferenceBench Leaderboard, the cheapest AI model with API access in 2026 is Qwen 2.5 1.5B at approximately $0.027 per million tokens for both input and output — with a Value score of 1862.0, the highest on the entire leaderboard. For the cheapest model with a verified quality score, Llama 3.2 3B costs $0.060 per million tokens, scores 55 on quality, runs at 154 tokens per second, and holds the Pareto Q×C×S badge — meaning no other model beats it on Quality, Cost, and Speed simultaneously.
The Cheapest AI Models on InferenceBench — Ranked
Here is the current cheapest tier on the InferenceBench leaderboard, sorted by input cost:
The tilde (~) next to $0.027 means the figure is a modelled estimate rather than a directly confirmed provider price. Verify before building production infrastructure around it.
The Absolute Cheapest — Qwen 2.5 1.5B
Qwen 2.5 1.5B sits at the top of the Value leaderboard with a score of 1862.0 — the highest value score of any model tracked on InferenceBench.
Model: Qwen 2.5 1.5B (Alibaba)
Params: 1.5B
Quality: Not yet verified on InferenceBench
Input: $0.027~ / M tokens (modelled estimate)
Output: $0.027~ / M tokens
Context: 32K tokens
Providers: Not currently listed
Value: 1862.0
The value score of 1862.0 is calculated from cost efficiency — at $0.027/M the model scores extremely high on the cost dimension of the composite metric regardless of quality score.
The honest assessment: Qwen 2.5 1.5B at 1.5 billion parameters is a small model. It is capable for simple classification, short-form summarization, and basic extraction tasks. For complex reasoning, long documents, code generation, or nuanced analysis, a larger model is required.
Best for: Simple classification, keyword extraction, short text summarization, basic Q&A where cost is the absolute priority.
The Cheapest Model With Verified Quality — Llama 3.2 3B
If you need a verified quality score alongside low cost, Llama 3.2 3B is the strongest option on the leaderboard.
Model: Llama 3.2 3B (Meta)
Params: 3.2B
Quality: 55 (verified)
Input: $0.060 / M tokens
Output: $0.060 / M tokens
Speed: 154 tok/s
Context: 128K tokens
Providers: 3 active
Value: 916.7
Badge: Pareto Q×C×S
The Pareto Q×C×S badge means no other model on the leaderboard beats Llama 3.2 3B simultaneously on Quality, Cost, and Speed. It is not the cheapest model but it is the cheapest model where the quality-cost-speed combination is unbeatable.
At 154 tokens per second it is also the fastest small model on the platform — important for latency-sensitive workloads.
Cost at scale:
GPT-4o output: $10.00 / M tokens
Llama 3.2 3B: $0.060 / M tokens
10M tokens/month:
GPT-4o: $100,000/month
Llama 3.2 3B: $600/month
Annual saving: $1,188,000
**Best for: **High-volume batch processing, simple summarization at scale, classification pipelines, any workload where cost efficiency matters more than frontier quality.
The Cheapest Model With Strong Quality — Qwen 2.5 7B
If your workload requires a quality score above 60, the cheapest option that delivers it is Qwen 2.5 7B.
Model: Qwen 2.5 7B (Alibaba)
Params: 7.6B
Quality: 70 (verified)
Input: $0.200 / M tokens
Output: $0.200 / M tokens
Speed: 27 tok/s
Context: 128K tokens
Providers: 4 active
Value: 350.0
Badge: Most Popular
It holds the Most Popular badge on InferenceBench — the model most selected by real developers on the platform. Quality score of 70 at $0.200/M covers the vast majority of production workloads.
Compared to GPT-4o at $2.50/M input and $10.00/M output, Qwen 2.5 7B delivers comparable quality for most standard tasks at 12.5x lower input cost and 50x lower output cost.
Best for: General chat, summarization, classification, structured extraction, RAG pipelines — the majority of real production AI workloads.
How Cheap Is Too Cheap? The Quality Trade-Off
The InferenceBench leaderboard makes the quality-cost trade-off visible at a glance.
Here is what the quality score difference between the cheapest models actually means in practice:
Quality 38 (Llama 3.2 1B at $0.030/M) Handles simple single-turn tasks. Struggles with multi-step reasoning, nuanced instructions, and complex document understanding. Best for basic classification and extraction where the task is well-defined and simple.
Quality 55 (Llama 3.2 3B at $0.060/M) Handles mid-complexity tasks reliably. Summarization, structured extraction, short-form Q&A, and classification with reasonable accuracy. Not suitable for complex reasoning or frontier-quality output.
Quality 57 to 58 (Qwen 3 4B, Qwen 2.5 3B at $0.100/M) Stronger instruction following and better output consistency than the 3B tier. Suitable for a wider range of production tasks including some conversational applications.
Quality 70 (Qwen 2.5 7B, Qwen 3 8B at $0.200/M) Covers the majority of production workloads with high reliability. This is where most teams find the right cost-quality balance in 2026.
Quality 87+ (GPT-4o at $2.50/M input, $10.00/M output) Required only for the most complex reasoning tasks — advanced multi-step agent workflows, nuanced legal or medical analysis, frontier code architecture. For everything else, quality 70 is sufficient.
How to Find the Cheapest Model for Your Workload on InferenceBench
The fastest way to find the cheapest model that fits your specific requirements:
Step 1: Open inferencebench.io/leaderboard/ and click the 💰 Cheapest badge filter — this surfaces all models sorted by lowest cost per million tokens.
Step 2: Click your task category tab — Chat, Code, Math, Reasoning, Vision, or Embedding. The cheapest model overall may not be the cheapest viable model for your specific task type.
Step 3: Set a quality floor in your evaluation. If your task requires quality 55 or above, eliminate models below that threshold. Use the Quality column to filter.
Step 4: Check the Providers column. A model with 1 active provider is a single point of failure. For the cheapest models — Qwen 2.5 1.5B and Llama 3.2 1B — verify current provider availability before building.
Step 5: Click ROI on any row to calculate your actual monthly cost at your projected token volume. The cost difference between $0.027/M and $0.200/M is dramatic at high volume:
100M tokens/month:
Qwen 2.5 1.5B ($0.027/M): $2,700/month
Llama 3.2 3B ($0.060/M): $6,000/month
Qwen 2.5 7B ($0.200/M): $20,000/month
GPT-4o output ($10.00/M): $1,000,000/month
Test Before You Commit
Cheap on paper does not mean right for your workload. Before committing to the cheapest model, validate it on your actual prompts.
Connect your provider accounts at inferencebench.io/playground/providers/, select the cheap model in Chat mode, and run your real domain prompts — not generic examples. The output quality difference between a $0.060/M model and a $0.200/M model is immediately visible on prompts that reflect your actual use case.
If the cheaper model output passes your quality bar on 80% or more of test cases, it is worth switching. If it fails on domain-specific edge cases, move up one tier and retest.
The Model Arena lets you run blind side-by-side comparisons between your current expensive model and a cheaper candidate — with identities hidden until after you vote. The results frequently show the cheaper model winning on your specific prompt types.
The Bottom Line
The cheapest AI model in 2026 costs $0.027 per million tokens. The cheapest model with a verified quality score costs $0.060/M. The cheapest model covering most production workloads costs $0.200/M.
The right cheap model is the one that passes your quality threshold at the lowest price — not the absolute cheapest one available. For most standard production workloads, that answer is Qwen 2.5 7B at $0.200/M or Llama 3.2 3B at $0.060/M, depending on how complex your tasks are.
The InferenceBench Leaderboard has 319 models with live daily pricing across 19 providers. The Cheapest badge filter surfaces every low-cost option in seconds. The ROI calculator tells you exactly what each one costs at your volume.
