Cost of Code Generation: AI Model Pricing Compared (May 2026)
Code generation is the highest-spend per-developer LLM workload in 2026. We price the canonical IDE-assistant generation (4K context in, 1.5K code out) across every major code-capable model.
The reference scenario
- Task: Code generation: 4K input tokens (code context + instruction) + 1.5K output tokens (generated code)
- Input tokens per call: 4,000 (file context, related modules, instruction)
- Output tokens per call: 1,500 (generated function or component)
- Monthly volume: 100,000 generations (active dev team using IDE assistant)
- Total tokens / month: 550M
Output ratio is high (1.5K out vs 4K in) because code generation is output-heavy compared to chat or summarization. That makes high output rates ($/1M output tokens) the dominant cost driver.
Cost across 10 models, sorted cheapest first
| Rank | Model | Per call | Per month | vs cheapest |
|---|---|---|---|---|
| 1 | DeepSeek V4 Flash | $0.000980 | $98.00 | — |
| 2 | Codestral | $0.0026 | $255 | 2.6x |
| 3 | Claude 3.5 Haiku | $0.0092 | $920 | 9.4x |
| 4 | DeepSeek V4 Pro | $0.0122 | $1,218 | 12.4x |
| 5 | Qwen 3.6 Plus | $0.0140 | $1,400 | 14.3x |
| 6 | Gemini 3.1 Pro | $0.0297 | $2,975 | 30.4x |
| 7 | Claude Sonnet 4 | $0.0345 | $3,450 | 35.2x |
| 8 | Claude Opus 4.7 | $0.0575 | $5,750 | 58.7x |
| 9 | GPT-5.5 | $0.0650 | $6,500 | 66.3x |
| 10 | GPT-5.5 Pro | $0.3900 | $39,000 | 398.0x |
Monthly spend at 100K generations
DeepSeek V4 Flash #................................... $98.00 Codestral #................................... $255 Claude 3.5 Haiku #................................... $920 DeepSeek V4 Pro #................................... $1,218 Qwen 3.6 Plus #................................... $1,400 Gemini 3.1 Pro ###................................. $2,975 Claude Sonnet 4 ###................................. $3,450 Claude Opus 4.7 #####............................... $5,750 GPT-5.5 ######.............................. $6,500 GPT-5.5 Pro #################################### $39,000
Per-call cost
DeepSeek V4 Flash #............................. $0.000980 Codestral #............................. $0.0026 Claude 3.5 Haiku #............................. $0.0092 DeepSeek V4 Pro #............................. $0.0122 Qwen 3.6 Plus #............................. $0.0140 Gemini 3.1 Pro ##............................ $0.0297 Claude Sonnet 4 ###........................... $0.0345 Claude Opus 4.7 ####.......................... $0.0575 GPT-5.5 #####......................... $0.0650 GPT-5.5 Pro ############################## $0.3900
Which model wins for code generation?
For frontier-quality coding: Claude Opus 4.7. It is the SWE-bench Verified leader, the model behind Cursor and Claude Code, and the consensus pick for agentic coding (multi-file refactors, debugging, novel implementations). It is also expensive — on the 100K/month scenario it lands around $5.7K. Worth it for the 20-30% of tasks where quality matters; overkill for the rest.
For routine work: DeepSeek V4 Pro. At $1.74 / $3.48 per 1M tokens it is roughly 5-10x cheaper than Claude Opus 4.7 with code-quality close enough that the gap rarely shows up on routine completions, simple test generation, or boilerplate scaffolding. Runner-up: Mistral Codestral, which is purpose-built for code with strong fill-in-the-middle support — ideal for IDE inline completion at $0.30 / $0.90 per 1M tokens.
The right answer is cascade. No production dev tool runs all traffic through Claude Opus 4.7 at scale — the bill would be insane. The pattern is: cheap model (DeepSeek V4 Pro or Codestral) for completions and trivial generations, frontier model (Claude Opus 4.7) for hard tasks the cheap model fails at. With a well-tuned cascade, all-in cost drops to 25-35% of frontier-only.
When to use a cheap model
- IDE inline completion (sub-200ms latency budgets)
- Boilerplate generation (CRUD endpoints, types from schemas, test scaffolds)
- Format conversions (JSON to TypeScript types, YAML to JSON)
- Single-file edits in well-typed languages (Go, Rust, Java)
- Generating documentation comments or README sections
When to use a frontier model
- Multi-file refactors (renaming a type across a codebase)
- Debugging non-trivial errors (race conditions, memory leaks)
- Novel implementations (new algorithm, new architecture)
- Agentic coding (planning + execution, tool use, iteration)
- Large-context work (40K+ tokens of context, repo-wide reasoning)
Output rates dominate code-generation cost
Code generation is output-heavy. On the 4K-in / 1.5K-out workload, output dominates total cost for every model in the comparison. That makes output $/1M tokens the metric to optimize against — not headline input price. GPT-5.5 Pro at $180/1M output is a 50x premium over DeepSeek V4 Pro at $3.48/1M output. On code, that gap is the bill.
Related
- Token Cost Calculator
- Cheap vs Expensive Model Comparison
- Model-Mixing Cost Savings
- AI Model Leaderboard
Pricing data sourced from official provider pages and OpenRouter, May 2026-05-06. Effective production cost will be 1.5-2x higher once you add system prompts, tool-call round-trips, and priority-tier surcharges. Self-hosted open-weight code models (Qwen 2.5 Coder, BGE-M3) are excluded from this view.