The cheapest coding-agent model is the one that gets a change through review with the lowest total bill, not necessarily the one with the lowest input rate. An agent repeatedly sends repository context, receives tool output, reasons, writes a patch, and may need several corrections. This comparison uses GetVibe’s live model catalog snapshot from October 3, 2026 and focuses on models that can be used with coding workflows.
Catalog price snapshot
The following figures are USD per one million tokens, input first and output second. The displayed price cells resolve from the GetVibe catalog; the date records when this comparison was checked.
| Model | Input / output rate | Best first test |
|---|---|---|
| GPT-6 Luna | $0.0055 input · $0.0274 output per 1M tokens | Small fixes, summaries, and fast iteration |
| GPT-6.1 Sol | $0.1096 input · $0.548 output per 1M tokens | Multi-step edits and stronger reasoning |
| GPT-6 Sol | $0.1096 input · $0.548 output per 1M tokens | Compare with 6.1 on existing prompts |
| Claude Sonnet 5.5 | $0.1734 input · $0.8667 output per 1M tokens | Messages-format coding and agent loops |
These are catalog rates, not a benchmark result. The model page remains the current source if rates change after October 3. Claude Sonnet 5.5 is useful for Claude Code because it accepts the Messages format; GPT-6 models should be tested through a Responses-compatible tool such as Codex CLI, not through Claude Code.
Use a coding test that measures completed work
Choose three repository tasks: a localized bug fix with a failing test, a small feature that touches multiple files, and a refactor with an explicit no-behavior-change requirement. Freeze the same Git commit, instructions, tool permissions, and test command for each run. Score whether the task is correct, whether tests pass, how many repair turns were needed, and how long the full change took.
Do not compare only the final answer. A model can produce a convincing patch that fails tests, causing another long turn. Record input and output usage for every turn, including tool results and repair prompts. Calculate cost per passing change. Run at least a few samples per task; one lucky response is not a stable ranking.
Why a cheap token rate can still cost more
Agents often resend a large prefix: system instructions, repository notes, tool schemas, and prior conversation. Output tokens can be a larger bill component than short prompts suggest. A model that needs two correction turns may be more expensive than one that costs more per token but produces a working patch in one pass. Cache reads can change repeated-input economics, but confirm that cached input is reported in usage before counting savings.
Keep context focused. Ignore build output, dependencies, generated snapshots, and unrelated packages. Ask the agent to inspect only likely files first. This reduces tokens and often improves attention. Use prompt caching guidance for stable repeated prefixes.
Which model should you start with?
- GPT-6 Luna: a sensible low-cost baseline for routine, well-scoped changes. Check the model page for context and features, then escalate tasks it fails.
- GPT-6.1 Sol: compare on tasks where planning and multi-file reasoning matter. It is priced below GPT-6 Sol in this catalog snapshot.
- GPT-6 Sol: keep it in the matrix if your existing prompts rely on its behavior; use measured results to decide whether a switch helps.
- Claude Sonnet 5.5: include it for Claude Code and Messages-based workflows. Its path and tool semantics differ from Responses-based Codex, so compare outcomes rather than pretending the clients are identical.
Make the comparison reproducible
Use the OpenAI-compatible API with a key in the environment for SDK-based clients:
export GETVIBE_API_KEY="your-dashboard-key"
python - <<'PY'
import os
from openai import OpenAI
client = OpenAI(base_url="https://getvibeapi.com/v1", api_key=os.environ["GETVIBE_API_KEY"])
result = client.chat.completions.create(
model="gpt-6-luna",
messages=[{"role": "user", "content": "Inspect src/parser.py and fix the failing edge case. Run the relevant test."}],
max_tokens=1200,
)
print(result.choices[0].message.content)
PYUse the exact endpoint the agent client supports. For Codex CLI, use the Responses API configuration; for Claude Code, use the Messages configuration. Preserve permission prompts and run agents in a branch so you can review a concrete diff.
Decision rule
Pick the least expensive candidate that meets your pass-rate target and latency budget on your own repository tasks. Keep the fixed evaluation set and rerun it when the catalog, agent version, or prompt changes. Store the date, model ID, usage, and test result with each observation. The October 3, 2026 catalog snapshot is linked through the model pages; use those pages for today’s prices instead of copying stale values into a spreadsheet.
Estimate total task cost
For planning, separate input and output usage. If a task uses 40,000 input tokens and 2,000 output tokens, calculate each leg with the selected model page’s current per-million rates, then add them. This is only a baseline: cached input, minimum request charges, multiple agent turns, and retries can change the real bill. Use the usage details after a test run to replace estimates with observed totals.
Do not confuse context size with billed tokens. A model’s maximum context describes what it can accept, not what each request costs. Keep the coding agent from loading the whole repository when only a few files are relevant. Selective context often improves answer quality and lowers the number of tokens sent.
Track quality signals that affect cost
For each run, capture whether the patch applies cleanly, whether relevant tests pass, how many tool calls fail, how many correction turns occur, and whether a reviewer accepts the change. A model that needs many retries can have a higher cost per accepted change even if its displayed token rate is lower. Latency matters too: the slowest route may be appropriate for overnight work but unsuitable for an interactive editor.
Separate model errors from harness errors. A missing test command, stale checkout, or permission denial should not be scored as a reasoning failure. Keep a trace with task ID, model ID, repository SHA, and redacted error category so a team can repeat the same comparison later.
Use a two-tier workflow only when evidence supports it
One cost strategy is to try a lower-cost model for routine edits and escalate a task after a deterministic signal, such as failing tests or invalid structured output. This can save money when most tasks are simple. It can also add a second full context pass on difficult tasks. Measure the escalation rate and the combined usage before deploying that strategy.
Keep fallback model selection explicit in application configuration. If reproducibility is important, report which model handled each task. Do not silently call a different public model while telling a developer the change came from the original model.