Every model page leads with a per-token price. That number is almost never what decides your bill. For coding agents in 2026, the deciding factor is how many output tokens a model burns to finish a task — and two models at the same sticker price can differ several-fold in real cost.
This is a deep dive on token economics for anyone running agentic coding at scale: why output dominates, a concrete efficiency gap from this month's releases, and five levers to pull. It pairs with our July 2026 model showdown.
Why output tokens dominate
Two structural facts stack up. First, providers price output tokens roughly 3–5× higher than input — GPT-5.6 Sol is $5 in / $30 out, Grok 4.5 is $2 / $6, Muse Spark 1.1 is $1.25 / $4.25. Second, agentic coding is output-heavy: the model narrates reasoning, emits tool calls, writes patches, re-reads results, and often loops several times per task. Input (your prompt and context) is paid once per call; output accrues every step of the loop.
So the model that "thinks out loud" for 60,000 tokens to land a fix can cost more than a pricier-per-token model that finishes in 16,000 — even before you factor the wall-clock time of generating all those tokens.
A concrete number from July 2026
xAI's Grok 4.5 disclosures put real figures on this. Grok 4.5 resolves an average SWE-Bench Pro task in about 15,954 output tokens, versus roughly 67,020 for Opus 4.8 at max effort — about 4.2× fewer. Combine that with Grok's lower $6/M output price and the per-task delta is stark:
| Model | Output tokens / task | Output $/M | Output cost / task* |
|---|---|---|---|
| Grok 4.5 | ~15,954 | $6.00 | ~$0.096 |
| Opus 4.8 (max) | ~67,020 | varies (premium) | several× higher |
*Output-only, illustrative: tokens × price ÷ 1,000,000. Input, caching, and retries change the total — the point is the multiplier, not a precise invoice.
Now multiply by volume. An agent doing 10,000 tasks/day at ~50,000 extra output tokens each is half a billion extra output tokens a day — the difference between a rounding error and a line item that shows up in the board deck.
The 2026 output-price landscape
| Model | Input $/M | Output $/M |
|---|---|---|
| GPT-5.6 Luna | $1.00 | $6.00 |
| Muse Spark 1.1 | $1.25 | $4.25 |
| Grok 4.5 | $2.00 | $6.00 |
| GPT-5.6 Terra | $2.50 | $15.00 |
| GPT-5.6 Sol | $5.00 | $30.00 |
Five ways to cut output-token spend
- Right-size the model. Don't run Sol ($30/M out) on tasks Terra or Luna can close. Tiering by difficulty is the single biggest lever.
- Cap reasoning effort. New effort controls (GPT-5.6's max/Pro mode) let you buy depth only where it pays. Default to lower effort; escalate on failure.
- Cache repeated context. Explicit prompt caching cuts the input side of every call that shares a big system prompt or codebase blob — and reduces re-reasoning over the same material.
- Tighten the agent loop. Fewer, larger, well-scoped steps beat many chatty ones. Add stop conditions, constrain diff size, and avoid letting the agent re-derive context it already has.
- Prefer token-efficient models for volume. For high-throughput agent fleets, a model that finishes in fewer output tokens (Grok 4.5's headline) can beat a "cheaper" one that rambles.
Make efficiency a first-class criterion
Benchmarks tell you if a model can do the task; token economics tell you if you can afford to run it a million times. In 2026, "output tokens per resolved task" belongs next to accuracy on your model-selection scorecard — measure it on your own workload, because the ratio shifts with prompt style, tool design, and task mix. The cheapest model isn't the one with the lowest sticker price; it's the one that finishes.
