Best LLMs for Coding in July 2026
Ranked by measured coding benchmarks, not editorial opinion, with current pricing and context windows alongside every entry.
Choosing a coding model involves a real tradeoff: the strongest models on code benchmarks are often an order of magnitude more expensive per token than models that score within a few points of them. This page ranks models by their measured performance on code-generation benchmarks (SciCode, Terminal-Bench Hard, and LiveCodeBench where published), so the quality side of that tradeoff rests on published evidence rather than vendor claims.
The quality index shown is the same per-task projection the Inferbase router consults when it routes coding traffic in production. Terminal-Bench Hard, an agentic coding evaluation that measures task completion in a real terminal, is part of that projection; benchmark publishers have been shifting their newest evaluation rounds toward it, so for the most recent frontier models it is often the coding evidence that exists. Where a model has no published result on a benchmark, the cell says so; absence of evidence is shown as absence, not filled in with a guess.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
- SciCode
- 60.2%
- Terminal-Bench Hard
- 62.9%
- Context
- 1M
- $ in / out per 1M
- $10 / $50
The ranking
This list is ordered by the same per-task quality projection the Inferbase router uses to route production traffic: benchmark results from independent suites are rank-normalized and combined per task. The quality index is a 0-100 percentile among the 164 models with measured evidence for this task. Models without published benchmark results here are not ranked, and not guessed at.
| # | Model | Quality index | SciCode | Terminal-Bench Hard | Context | $ in / out per 1M |
|---|---|---|---|---|---|---|
| 1 | anthropic | 99.6 | 60.2% | 62.9% | 1M | $10 / $50 |
| 2 | openai | 99.1 | 56.1% | 65.9% | 1.1M | $5 / $30 |
| 3 | openai | 98.3 | 56.1% | 60.6% | 1.1M | $5 / $30 |
| 4 | openai | 97.7 | 56.6% | 57.6% | 1.1M | $2.5 / $15 |
| 5 | google | 97.2 | 58.9% | 53.8% | 1.0M | $2 / $12 |
| 6 | openai | 95.9 | 53.9% | 57.6% | 1.1M | $2.5 / $15 |
| 7 | anthropic | 95.7 | 53.5% | 58.3% | 1M | $5 / $25 |
| 8 | anthropic | 94.5 | 53.6% | No data | 1M | $2 / $10 |
| 9 | anthropic | 93.7 | 54.5% | 51.5% | 1M | $5 / $25 |
| 10 | openai | 93.4 | 53.2% | 53.0% | 272K | $1.75 / $14 |
marks models served through the Inferbase API. Missing values are shown as No data rather than estimated.
Also worth evaluating
The next ranks after the top ten, for teams that want a wider shortlist.
Or stop choosing manually
This page exists because model choice is a per-task decision, and the honest answer changes as benchmarks and prices move. Routing makes that decision per request instead.
Send model="auto" to one OpenAI-compatible endpoint and each request is served by the best fit from the same quality and price data behind this ranking, with the decision disclosed per request.
from openai import OpenAI
client = OpenAI(
base_url="https://api.inferbase.ai/api/v1/inference",
api_key="YOUR_INFERBASE_KEY",
)
# Let routing pick the best model per request,
# or pin any model id from GET /models.
response = client.chat.completions.create(
model="auto",
messages=[{"role": "user", "content": "..."}],
)Frequently asked questions
How this ranking works and how to act on it.
Benchmark results on SciCode, Terminal-Bench Hard, and LiveCodeBench (where published) are rank-normalized within each suite and combined into a per-task quality score, the same projection the Inferbase router uses to route coding requests in production. The displayed index is a 0-100 percentile among models with measured coding evidence.
Models appear only when a covered benchmark suite has published code-generation results for them. A missing model is not a judgment; it means there is no measured evidence to rank it on, and this page does not substitute estimates for evidence.
No. The ranking regularly shows models within a few index points of the leader at a fraction of the per-token price. For bulk coding workloads such as boilerplate generation or test scaffolding, a mid-ranked model is often the economically rational pick.
Models marked as served on Inferbase are available through a single OpenAI-compatible endpoint. You can pin any served model by id, or send model="auto" and let the router pick per request based on the same quality data behind this page.
The underlying benchmark and pricing data refresh on the catalog sync cycle, and the page regenerates at least hourly from that data. The date in the title reflects the current data, not an editorial revision.
Related rankings
The same evidence-first method, applied to other tasks.
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