What AI-Assisted Coding Actually Costs in 2026
We calculated the real monthly cost of using GPT-5, Claude Sonnet, Gemini, and open-source models for coding. The price gaps are bigger than you think.
Everyone publishes per-token pricing tables. Nobody does the math on what you'll actually spend.
We took six popular AI models, ran them through three real coding workloads, and calculated the monthly bill. The results: your choice of model can mean the difference between $0.50/month and $2,000/month for the same number of API calls.
Here's the breakdown.
The Models We're Comparing
We picked six models that developers actually use for coding, spanning budget to flagship:
| Model | Input $/1M | Output $/1M | Context | Category |
|---|---|---|---|---|
| DeepSeek V3 | $0.14 | $0.28 | 164K | Open-source |
| Gemini 2.5 Flash | $0.15 | $0.60 | 1M | Budget |
| GPT-4o Mini | $0.15 | $0.60 | 128K | Budget |
| Claude Sonnet 4.6 | $3.00 | $15.00 | 200K | Mid-tier |
| GPT-5 | $1.25 | $10.00 | 128K | Flagship |
| Claude Opus 4.6 | $5.00 | $25.00 | 200K | Flagship |
Prices are per 1 million tokens as of February 2026, sourced from official API pricing pages (Anthropic, OpenAI, Google, DeepSeek). We're excluding reasoning models (o3, DeepSeek R1) because their thinking tokens make cost prediction unreliable. That's a separate analysis.
How We Estimated Token Usage
Before the cost tables mean anything, you need to know how many tokens coding work actually burns.
A typical code file runs 200-400 tokens. A prompt with context (file contents, instructions, error messages) is 1,000-3,000 tokens for a focused task, or 10,000-50,000 tokens when you feed in multiple files for refactoring.
Responses are shorter: a function is 200-500 tokens, a full file rewrite is 1,000-3,000 tokens.
We defined three developer profiles based on these patterns.
Scenario 1: Solo Developer — Side Projects and Freelance
Profile: 30-50 prompts per day, 5 days a week. Mix of code generation, debugging, and code review. Average prompt is ~2,000 input tokens (a file plus instructions) and ~800 output tokens (a function or fix).
Monthly token consumption: ~2M input tokens, ~800K output tokens.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| DeepSeek V3 | $0.28 | $0.22 | $0.50 |
| Gemini 2.5 Flash | $0.30 | $0.48 | $0.78 |
| GPT-4o Mini | $0.30 | $0.48 | $0.78 |
| GPT-5 | $2.50 | $8.00 | $10.50 |
| Claude Sonnet 4.6 | $6.00 | $12.00 | $18.00 |
| Claude Opus 4.6 | $10.00 | $20.00 | $30.00 |
At this volume, the budget tier is essentially free. DeepSeek V3 at 50 cents per month is hard to argue against for side projects. The real question: is the quality gap worth 36x the price?
For solo work where you're reviewing every output anyway, budget models handle 80% of coding tasks fine. Use Claude Sonnet 4.6 when you hit a wall on a hard debugging problem, then switch back to DeepSeek or Flash for the routine stuff.
Scenario 2: Startup Team — Production Codebase
Profile: 3 developers, each making 80-120 prompts per day. Heavier context: feeding in 3-5 files per prompt for refactoring, architecture questions, and PR reviews. Average prompt is ~8,000 input tokens with ~2,000 output tokens.
Monthly token consumption: ~18M input tokens, ~4.5M output tokens (combined across the team).
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| DeepSeek V3 | $2.52 | $1.26 | $3.78 |
| Gemini 2.5 Flash | $2.70 | $2.70 | $5.40 |
| GPT-4o Mini | $2.70 | $2.70 | $5.40 |
| GPT-5 | $22.50 | $45.00 | $67.50 |
| Claude Sonnet 4.6 | $54.00 | $67.50 | $121.50 |
| Claude Opus 4.6 | $90.00 | $112.50 | $202.50 |
This is where the split gets interesting. Budget models keep total costs under $6/month for a team of three. Flagships push past $100.
But there's a catch: at this scale, quality gaps compound. A budget model that gets a refactoring wrong costs developer time to fix. If your team is doing complex architecture work in a large codebase, Claude Sonnet 4.6 at $121.50/month ($40.50 per developer) is cheaper than one hour of wasted debugging.
Route requests by complexity. Use GPT-4o Mini or Gemini 2.5 Flash for boilerplate, tests, and documentation. Escalate to GPT-5 or Claude Sonnet 4.6 for architecture decisions and bug hunts. If 80% of your requests go to a budget model and 20% go to GPT-5, your Scenario 2 bill drops from $67.50 to about $18/month. That's the hybrid advantage.
Scenario 3: AI-Heavy Workflow — Agentic Coding and CI/CD
Profile: AI agent running autonomously. CI pipelines generating tests, code review bots, automated refactoring. 500+ model calls per day with large context windows. Average prompt is ~20,000 input tokens (full file trees, test suites, PR diffs) with ~4,000 output tokens.
Monthly token consumption: ~200M input tokens, ~40M output tokens.
| Model | Input Cost | Output Cost | Monthly Total |
|---|---|---|---|
| DeepSeek V3 | $28.00 | $11.20 | $39.20 |
| Gemini 2.5 Flash | $30.00 | $24.00 | $54.00 |
| GPT-4o Mini | $30.00 | $24.00 | $54.00 |
| GPT-5 | $250.00 | $400.00 | $650.00 |
| Claude Sonnet 4.6 | $600.00 | $600.00 | $1,200.00 |
| Claude Opus 4.6 | $1,000.00 | $1,000.00 | $2,000.00 |
At scale, flagship pricing gets painful fast. Claude Opus 4.6 at $2,000/month is a real infrastructure cost. DeepSeek V3 does the same volume for $39.
But "same volume" doesn't mean same results. For agentic workflows where the model operates without human review, accuracy matters more, not less. A model that generates a subtly wrong test or misses an edge case in a code review creates downstream bugs. Gemini 3.1 Pro at $2/$12 per 1M tokens is worth considering here: it scores 95.0% on HumanEval (matching the flagships) with a 1M native context window, and its total cost for this scenario would be around $880/month. That's a middle ground between budget and premium.
For CI/CD pipelines: DeepSeek V3 or Gemini 2.5 Flash for test generation and linting. GPT-5 for code review where accuracy matters. Don't run Opus-class models in automated pipelines unless you've measured the quality difference and confirmed it justifies the 50x cost multiplier.
The Output Token Tax
One thing the per-token pricing tables don't make obvious: output tokens cost 3-5x more than input tokens across every provider. This matters because coding prompts are input-heavy (you paste a lot of code) but the bill is output-heavy (the model's response costs more per token).
Look at Claude Sonnet 4.6 in Scenario 2: $54 on input, $67.50 on output, even though the model consumed 4x more input tokens. Output pricing is the hidden multiplier.
Models with lower output-to-input ratios give you more predictable bills. DeepSeek V3 has a 2:1 ratio ($0.28 vs $0.14). Claude Opus 4.6 has a 5:1 ratio ($25 vs $5). When your agent starts generating long responses, that ratio stacks up.
What the Benchmarks Say About Quality
Cheap doesn't mean bad. Here's how these models score on coding benchmarks tracked by EveryRank and sourced from provider evaluations:
| Model | HumanEval | MMLU-Pro | GPQA |
|---|---|---|---|
| Claude Opus 4.6 | 95.0% | 89.5% | 75.5% |
| GPT-5 | 95.0% | 88.5% | 73.5% |
| Claude Sonnet 4.6 | 94.0% | 86.0% | 70.0% |
| GPT-4o Mini | 87.2% | 68.0% | — |
| DeepSeek V3 | 89.0% | 78.0% | — |
| Gemini 2.5 Flash | 89.5% | 76.0% | — |
The gap between budget tier (87-89% on HumanEval) and flagships (94-95%) is real but smaller than the pricing gap suggests. You're paying 20-50x more for a 5-8 percentage point improvement on code generation.
For autocomplete, boilerplate, and test writing, 87% is fine. For complex multi-file refactoring, that extra 7% is the difference between a working solution and a subtle bug.
Our Recommendations
Under $20/month budget: Use DeepSeek V3 or Gemini 2.5 Flash as your daily driver. Both score above 89% on HumanEval at under $1/month for solo use. If you hit a hard problem, switch to Claude Sonnet 4.6 for that one session. A few cents won't break the bank.
$20-100/month budget: Claude Sonnet 4.6 is the best value in this range. It scores 94% on HumanEval, handles complex multi-file context, and costs $18/month for a solo developer. GPT-5 at $10.50/month is the other solid pick if you prefer OpenAI's ecosystem.
$100+/month budget: You should be routing by complexity, not using one model for everything. Pair a budget model for routine work with Gemini 3.1 Pro or Claude Sonnet 4.6 for complex tasks. Going all-in on Opus only makes sense if you're building agentic systems where every output must be correct without human review.
Skip GPT-4o. At $2.50/$10.00 per 1M tokens, it's more expensive than GPT-5 ($1.25/$10.00) while scoring lower on every benchmark. It exists for backward compatibility, not as a good deal.
The Bottom Line
The pricing spread across AI coding models is 50x from cheapest to most expensive. But the quality spread is 5-8 percentage points on coding benchmarks. Most developers are overpaying by using a single flagship model for all tasks.
The move that actually saves money: pick two models. A budget model (DeepSeek V3, Gemini 2.5 Flash, or GPT-4o Mini) for the 80% of tasks that don't need top-tier reasoning, and a mid-tier or flagship model (Claude Sonnet 4.6, GPT-5) for the 20% that do. Run the numbers on EveryRank's pricing page for your own usage patterns.
Your AI bill should look more like your cloud bill: optimized by workload, not one-size-fits-all.