Data Analysis: 113 AI Coding Models Ranked by Output/Input Price Ratio (2026 Snapshot)
By Eric Bush · July 17, 2026 · 9 min read
The Ratio Nobody Talks About
When developers compare AI coding models, they compare input token prices. Output tokens get a passing glance. But output prices are usually 4-6x higher than input prices, and for code generation — where output tokens dominate — the ratio between output and input pricing determines whether a model is actually cheap for your workload.
We looked at all 113 AI coding models tracked in our cost estimator database and computed the output/input price ratio for each. The spread is enormous: from 1.5x (DeepSeek V3.2) all the way to 10x (Perceptron Mk1). Same $100 of input tokens on two models can cost either $150 or $1,000 in output — the ratio is that decisive.
Distribution of Output/Input Ratios Across 113 Models
Grouped by ratio bucket:
| Ratio bucket | Model count | % of catalog | Providers most represented |
|---|---|---|---|
| Under 3x (output-friendly) | 16 | 14% | xAI, DeepSeek, Xiaomi, Poolside |
| 3x to 5x (balanced) | 42 | 37% | Alibaba, Mistral, Moonshot, MiniMax |
| 5x to 7x (output-heavy) | 43 | 38% | Anthropic, Google, Cursor |
| 7x plus (output-hostile) | 12 | 11% | OpenAI, Google (Pro tiers) |
Three quarters of coding models charge 3-7x more for output than input. The two ends of the distribution reveal provider strategies: xAI and DeepSeek keep the ratio low to encourage code generation workloads, while OpenAI's flagship line hits 8x on the reasoning tier because they price to reflect the cost of test-time compute.
Under 3x: The Output-Friendly Tier
These models are cheapest for workloads that generate a lot of output tokens per input token — code generation from short specs, long reports from short queries, or verbose refactors of small changes.
| Model | Provider | Input $/M | Output $/M | Ratio |
|---|---|---|---|---|
| DeepSeek V3.2 | DeepSeek | $0.229 | $0.343 | 1.50x |
| Grok 3 Mini | xAI | $0.30 | $0.50 | 1.67x |
| DeepSeek V4 Flash | DeepSeek | $0.09 | $0.18 | 2.00x |
| Grok Build 0.1 | xAI | $1.00 | $2.00 | 2.00x |
| DeepSeek V4 Pro | DeepSeek | $0.435 | $0.87 | 2.00x |
| Grok 4.20 Multi-Agent | xAI | $1.25 | $2.50 | 2.00x |
| Granite 4.1 8B | IBM | $0.05 | $0.10 | 2.00x |
| MiMo V2.5 | Xiaomi | $0.105 | $0.28 | 2.67x |
DeepSeek's entire V4 series and xAI's Grok family dominate this tier. If you have code generation as your primary workload — generating classes, functions, tests from short prompts — these models keep your output-heavy bill under control.
7x Plus: The Output-Hostile Tier
These models charge disproportionately for output. They're designed for workloads that consume a lot of input and produce short outputs — code review, log analysis, classification, and retrieval-augmented Q&A. Using them for verbose code generation is expensive.
| Model | Provider | Input $/M | Output $/M | Ratio |
|---|---|---|---|---|
| Perceptron Mk1 | Perceptron | $0.15 | $1.50 | 10.00x |
| Gemini 2.5 Flash | $0.30 | $2.50 | 8.33x | |
| Ring-2.6-1T | inclusionAI | $0.075 | $0.625 | 8.33x |
| GPT-5 | OpenAI | $1.25 | $10 | 8.00x |
| GPT-5.2 | OpenAI | $1.75 | $14 | 8.00x |
| Gemini 2.5 Pro | $1.25 | $10 | 8.00x | |
| Qwen3 Coder | Alibaba | $0.22 | $1.80 | 8.18x |
OpenAI's GPT-5 family stands out here. The 8x ratio is intentional — these models are tuned for reasoning-heavy short outputs, and OpenAI's pricing reflects the extra test-time compute burned to generate them.
Matching Ratios to Workloads
The practical takeaway: workload shape should drive model selection at least as much as headline input price. Rough guidelines:
| Workload | Output/input token ratio | Target model ratio |
|---|---|---|
| Code generation from short spec | Output-heavy (3-8x) | Under 3x (DeepSeek, Grok) |
| Refactor small files | Balanced (1-2x) | 3-5x (Alibaba, Mistral, Moonshot) |
| Repository-wide code review | Input-heavy (0.05-0.2x) | 5-7x (Anthropic, Google) or higher |
| Bug fix with repo context | Input-heavy (0.1-0.3x) | 5-8x (Anthropic, OpenAI Pro) |
| Documentation from code | Output-heavy (2-4x) | Under 3x or balanced 3-5x |
Why This Matters More in 2026
Six months ago, most coding models had ratios between 3x and 5x. The market has since split: providers targeting cost-sensitive coding workloads (DeepSeek, xAI, IBM Granite) have pushed ratios below 2x, while providers targeting reasoning-heavy work (OpenAI, Google Pro tier) have pushed above 8x. Understanding which end you're paying for is now worth more real money.
A team spending $5,000 per month on a 7x-ratio model that mostly does code generation could probably move to a 2x-ratio model and cut that bill by 40-60%, holding output quality reasonably constant with 2026's mid-tier alternatives. That's the size of savings that pays for a monthly cost audit.
Bottom Line
Output-to-input price ratio is the second most important number after headline input price. For code-generation-heavy workloads, models below 3x — DeepSeek V4 Pro, Grok Build 0.1, MiMo V2.5 — offer structural advantages. For code-review or Q&A heavy workloads, higher ratios are fine because you're not generating many output tokens. Plug your actual workload shape into our cost calculator to see the effect of switching model ratios in your own numbers.
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Frequently Asked Questions
What is the output/input price ratio for AI models?
It's the output token price divided by the input token price. A model priced at $1 per million input tokens and $5 per million output tokens has a 5x ratio. This number determines how much your bill changes when you generate more code (output tokens) versus feed more context (input tokens).
Which AI coding models have the lowest output/input ratio?
DeepSeek V3.2 has the lowest at 1.5x. Several xAI Grok models, DeepSeek V4 Pro and V4 Flash, Poolside Laguna, Xiaomi MiMo V2.5 Pro, and IBM Granite 4.1 8B sit at exactly 2x. These models are structurally cheap for code-generation-heavy workloads.
Why is OpenAI's GPT-5 ratio so high (8x)?
GPT-5's 8x output/input ratio reflects the extra test-time compute burned during reasoning steps. When the model 'thinks' before generating a final answer, those reasoning tokens are output tokens. OpenAI prices output tokens high to reflect that compute cost. For reasoning-intensive tasks where reasoning tokens produce real value, the price is defensible; for straight code generation, it's expensive.
How do I know which ratio matches my workload?
Sample a few weeks of your API usage logs and compute the average ratio of output tokens to input tokens per call. Code generation from short specs typically runs 3-8x output-heavy. Code review of large repos runs 0.05-0.2x, meaning input dominates. Match your workload's ratio to a model whose price ratio is inverse — output-heavy workloads want low-ratio models.
Can switching to a lower-ratio model really cut costs by 40-60%?
Yes, in the specific case of output-heavy workloads currently on high-ratio models. A team generating 500k output tokens per day on GPT-5 ($10/M output) pays $150/month for output. Moving to Grok Build 0.1 ($2/M output) drops that to $30/month. Real-world savings depend on quality tradeoffs — always A/B test before switching.
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