Best LLMs Overall in July 2026
The general-purpose ranking: ordered by composite intelligence evidence, with the price and context data needed to judge the tradeoff.
A single general-purpose ranking hides more than it reveals, which is why this site also maintains per-task pages for coding, research, and specific capabilities. Still, a defensible generalist ordering is useful as a starting point, and this page provides one: models ranked by composite intelligence-index evidence, shown with current pricing so the cost of each quality tier is visible.
The practical takeaway from this table is usually not "use the number one model for everything" but the size of the price gap between adjacent quality tiers. That gap is the entire economic case for routing requests to the model each one actually needs.
The top pick
The strongest measured fit for this task right now, taken from the live ranking below.
- Intelligence Index
- 59.9
- 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 169 models with measured evidence for this task. Models without published benchmark results here are not ranked, and not guessed at.
| # | Model | Quality index | Intelligence Index | Context | $ in / out per 1M |
|---|---|---|---|---|---|
| 1 | anthropic | 100 | 59.9 | 1M | $10 / $50 |
| 2 | openai | 99.4 | 58.9 | 1.1M | $5 / $30 |
| 3 | anthropic | 98.8 | 55.7 | 1M | $5 / $25 |
| 4 | openai | 98.2 | 55.0 | 1.1M | $2.5 / $15 |
| 5 | openai | 97.6 | 54.8 | 1.1M | $5 / $30 |
| 6 | anthropic | 97.1 | 53.5 | 1M | $5 / $25 |
| 7 | anthropic | 96.5 | 53.4 | 1M | $2 / $10 |
| 8 | openai | 95.9 | 51.4 | 1.1M | $2.5 / $15 |
| 9 | openai | 95.3 | 51.2 | 1.1M | $1 / $6 |
| 10 | zai | 94.7 | 51.1 | 1.0M | $0.93 / $3 |
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.
It is a composite of many individual evaluations published by an independent benchmark suite, rank-normalized across the catalog. It is the generalist signal used when a task has no dedicated benchmark, both on this page and inside the Inferbase router.
Because most production requests are not frontier-difficulty. Classification, extraction, short summaries, and routine Q&A are served indistinguishably well by models far down this table at a fraction of the price. The top model is the right tool for the hardest slice of traffic, not all of it.
The gap has narrowed substantially: current open-weight models place high in this ranking while costing dramatically less per token, and they can be self-hosted. The table makes the current state of that comparison concrete rather than anecdotal.
From independent benchmark suites ingested into the Inferbase catalog and projected into per-task quality scores, the same data path the production router uses. Vendor self-reported numbers are not the ranking basis.
It regenerates at least hourly from live catalog data, and materially reorders when a new model publishes benchmark results or a price changes. The month in the title tracks the data, not a manual rewrite.
Related rankings
The same evidence-first method, applied to other tasks.
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