2026 LLM Comprehensive Capability Leaderboard

How the ranking works

Data as of 2026-08-05Scenarios — coding, long-form writing, Chinese/open-source, and agent — are graded S/A/B, based mainly on the Arena (arena.ai) August 1, 2026 snapshot and the Artificial Analysis Intelligence Index. Public figures are in the table below.

Source notes

Arena and Artificial Analysis use different weighting, so their placements won't necessarily match; the public figures are below.

July addendum: Kimi K3 has launched and gone open-source, and the GPT-5.6 family is rolling into the Arena one model at a time — with thin vote counts, the standings are unstable. For Chinese models we're not applying the old SuperCLUE May 2026 scores; instead, we're looking at the relative positions of Qwen / Kimi / DeepSeek / GLM / Doubao on the Arena.

Divided into S / A / B by scenario

The tiers are an editorial judgment, not officially certified:

• S:In this scenario, the public leaderboards and our daily usage both hold up, so these can serve as the default go-to candidates.
• A:Exceptionally strong in one area, or standing out on price-performance / open-source controllability, but with clear weaknesses (few votes / preliminary, expensive, slow, or missing modalities).
• B:Gets the job done, suitable as a fallback or for specialized tasks; one screenshot isn't enough to elevate it to the company's sole default.

The four tables below are split by scenario. The "Basis" column only restates the Arena / AA signals and the editor's tiering rationale from above; it introduces no new benchmark scores.

Coding & Frontend Delivery

On Arena Code/WebDev Overall (Aug 1), Opus 5-max is around 1705 Elo, ranking first; Kimi K3-max is around 1676; Qwen3.8-max WebDev around 1668 (Preliminary); GPT-5.6 Sol around 1620. On the Text Coding sub-list, Fable 5 is around 1553±9still sits in the top tier. A strong WebDev ranking doesn't mean it's first at everything.

Long-context writing, synthesis, and multimodal reading assistance

On crowd-pleasing dimensions such as Overall, Creative Writing, and Instruction Following, the Claude line has consistently ranked near the top; Gemini / GPT each have their own selling points in long context, multimodal, and product integration. Before picking a model, ask yourself: are you optimizing for blind-test perception or task pass rate?

Chinese-language business and open-source controllability

Don't blindly apply SuperCLUE's May scores. Check the relative positions of Qwen / Kimi / DeepSeek / GLM / Doubao on Arena, then layer on your business corpus. Being "second on the leaderboard" as an open-source model doesn't extrapolate to "runs fine in your server room."

Tool calling and agentic direction

The AA Intelligence Index weighs agentic tasks more heavily; Computer Use / multi-step tool chains are Anthropic's product-side focus. If subscores haven't been independently rechecked, we won't invent percentages here. The most common failure: treating "a working demo" as "production-ready."

Five pitfalls to avoid when reading the leaderboard

1. Style preferences don't extrapolate to correctness:The Arena is a blind human vote for which response people prefer. A model with a likable tone and polished formatting won't necessarily lead on rigorous reasoning tasks.
2. Preliminary labels and wide confidence intervals:Models with few votes—some of the new max entries have only a few thousand—can jump three rungs in a day. When you see a ±15–20 interval, assume votes are still rolling in and the ranking isn't stable.
3. Contamination and benchmark gaming:Public static benchmarks (MMLU-Pro, GPQA, etc.) get criticized for contamination year after year; we outright ignore any "perfect score" screenshots that lack a date and reproduction notes.
4. A closed API isn't the same model as the chat version:Under the same brand, chat-latest, thinking, max effort, and codex-harness sit on different rows of the leaderboard. Write the wrong tier into a procurement contract and you've effectively bought a different model.
5. The hidden costs of open-source weights:For an oversized MoE like K3, the weight footprint and multi-machine inference costs turn "second on the leaderboard" into "we can't run it in our data center." DeepSeek / GLM may actually be the more S-tier pick when it comes to deployability.

Modify our version with the template

The above is the editorial opinion of the OBPAI geek team. The template "AI Large Language Model (LLM) Overall Capability Ladder Chart" (ID 1785671363439) provides draggable tiles—just drag them to build the model leaderboard you think is right.

For embedding steps, see How to Embed the Interactive Ladder Chart。

How to Read the Public Leaderboard in Early August

In early August 2026, the overall popularity leaderboard is still crowded at the top with Anthropic models; on the Synthetic Intelligence Index, Opus 5, Fable 5, and GPT-5.6 Sol are bunched tightly together; and on the Coding Delivery leaderboard, both Kimi K3 and Opus 5 warrant serious A/B testing. When choosing, first define the use case — coding, long-form text, Chinese, or agent work — then decide whether you can live with closed-source models and the resulting bills. Rankings refresh weekly, so defer to the leaderboard you yourself pulled up.