Agentic coding is token-hungry: one autonomous task can mean dozens of model calls — planning, editing, reading test output, revising. That makes the price per million tokens the most important number in your AI tooling budget, and in July 2026 the spread between providers is enormous. Here are the real numbers, and how BYOK (bring your own key) routing turns them into a 10× cost advantage. Prices below are as of July 2026 — always check provider pages, they change fast (usually downward).
What the models actually cost (per million tokens)
- DeepSeek V4 Flash: $0.14 input / $0.28 output. With DeepSeek's context caching, cached input drops to $0.0028 — a 98% discount that matters enormously for agents, which resend project context on every loop iteration.
- DeepSeek V3.2 (legacy): $0.28 / $0.42 — still available, still absurdly cheap.
- DeepSeek V4 Pro: $1.74 / $3.48, with periodic 75% promotions taking it to $0.435 / $0.87 — near-flagship reasoning at a fraction of Western lab prices.
- Claude Sonnet 4.6: $3 / $15 — the workhorse frontier tier.
- GPT-5.5: $5 / $30 — top-shelf OpenAI pricing.
- Gemini 2.5 Flash: $0.30 / $2.50 — Google's budget tier, undercut by DeepSeek on both sides.
Read that spread again: on output tokens, GPT-5.5 costs 107× more than DeepSeek V4 Flash. They are not 107× apart in coding ability — on routine agentic work (implement, run tests, fix, repeat) the cheap tier completes most tasks fine, and the loop's verification catches the ones it doesn't.
What a real agentic task costs
Take a plausible mid-size task: an agent burns 2M input tokens (mostly re-read context) and 300K output tokens across a full loop.
- On DeepSeek V4 Flash: 2M × $0.14 + 0.3M × $0.28 ≈ $0.36 — and with cache hits on repeated context, realistically closer to $0.10.
- On Claude Sonnet 4.6: 2M × $3 + 0.3M × $15 ≈ $10.50.
- On GPT-5.5: 2M × $5 + 0.3M × $30 ≈ $19.00.
Run five such tasks a day, 20 working days a month: ~$10–36/month on DeepSeek versus ~$1,000–1,900 on frontier models. This is why bundled-inference IDEs moved to credit systems — Cursor's June 2026 change from unlimited agent use to a 1,500-credit monthly allowance reflects what heavy agentic use actually costs the vendor. Under BYOK, that economics problem is yours to optimize instead of theirs to throttle.
The strategy: route by difficulty, verify everything
The optimal setup isn't "cheapest model always" — it's a portfolio:
- Bulk loop work → cheap tier. DeepSeek V4 Flash, Qwen, or MiMo for implementation iterations, test-fixing, refactors. In Cortex all three are first-class BYOK providers.
- Hard problems → frontier tier. Claude or GPT for architecture decisions, gnarly debugging, security-sensitive code. Switch models per-task from the dropdown.
- Let verification equalize quality. A verified loop — real tests, real exit codes, second-model review — catches cheap-model mistakes automatically, which is precisely what makes cheap models safe to use for bulk work. (The data on why unverified output can't be trusted at any price: the 29% trust problem.)
- Set hard budgets. Cortex's loop engine enforces token and dollar caps per run — the agent stops the moment a budget is exceeded, so a runaway loop can't surprise you at invoice time. Provider-side spend limits are good hygiene on top.
Because Cortex is BYOK with no markup — you pay providers directly; the IDE never resells tokens — the numbers above are your actual costs. The IDE is free; Pro ($10/month) covers hosted extras (web search, embeddings, OCR, MCP), not inference.