Coding Assistant Cost — Self-Hosted Copilot for 100 Engineers
A coding assistant for 100 engineers costs $1,800–$22,000 per month — Sonnet-tier is the sweet spot.
Scenario
An internal coding assistant integrated into the IDE for 100 engineers. Each engineer triggers ~200 completions per day — a mix of inline autocomplete, chat questions, and refactor suggestions. Average request includes 2,000 tokens of code context + the prompt; the model returns ~500 tokens of generated code or explanation.
| Assumption | Value |
|---|---|
| Engineers | 100 |
| Requests / engineer / day | 200 |
| Working days / month | 20 |
| Input / request | ~2,000 tokens (code context) |
| Output / request | ~500 tokens |
| Cache hit rate | 30% (code context partially repeats) |
Coding workloads are output-heavy compared to chat (1:4 vs 1:8 ratio). Function calling support is critical for tool use (run code, search files, run tests).
Monthly cost across recommended models
Calculated at 800M input tokens + 200.0M output tokens, with 30% prompt cache hit rate.
| Model | Input cost | Output cost | Cache savings | Total / mo |
|---|---|---|---|---|
| Deepseek Chat Cheapest | $224 | $84.00 | −$60.48 | $248 |
| Qwen3 Coder | $176 | $190 | — | $366 |
| Gpt 5 | $1000 | $2000 | −$270 | $2730 |
| Claude Sonnet 4 | $2400 | $3000 | — | $5400 |
💡 Switching from Claude Sonnet 4 to Deepseek Chat saves $5152/month (95% reduction).
Why these models
Coding rewards model quality more than chat does — bad code suggestions waste engineer time worth $50+/hour. Claude Sonnet 4 hits the price/quality knee. GPT-5 is the premium pick for hardest problems. DeepSeek and Qwen3-Coder are 5-10× cheaper if your team is OK with slightly lower quality on complex refactors. Avoid Haiku-tier — too many wrong suggestions kills adoption.
Key insights
- 1. Engineer productivity dwarfs model cost. If a $10/engineer/month upgrade saves 30 minutes/week, it pays back 20×.
- 2. Sample 10% of requests through GPT-5 or Opus to A/B test quality differences — most teams over-pay because they never measured.
- 3. Cache repeated code context aggressively (open file, recent edits). Real-world cache hit on coding tools can reach 50%+ with smart context management.
- 4. For specialized teams (data science, infra), consider routing per language — Qwen3-Coder beats GPT-5 on some Python benchmarks at 1/10 the cost.
Cost at different scales
| Scale | Deepseek Chat | Qwen3 Coder | Gpt 5 | Claude Sonnet 4 |
|---|---|---|---|---|
| 10 engineers (pilot) | $24.75 | $36.60 | $273 | $540 |
| Baseline (100 engineers) | $248 | $366 | $2730 | $5400 |
| 1,000 engineers (mid-sized) | $2475 | $3660 | $27.3k | $54.0k |
| 10,000 engineers (FAANG) | $24.8k | $36.6k | $273.0k | $540.0k |
Try your own scenario
The numbers above use our best-guess assumptions. For your actual workflow, use the interactive calculator to plug in your real token volumes and quality requirements.
All cost figures are estimates based on publicly-listed pricing as of the data refresh date. Verify with the provider's official pricing page before making business decisions. Embedding costs, vector database costs, and infrastructure costs are not included unless explicitly noted.