AI Model Benchmark Hub

Understand model leaderboards without blindly chasing first place

This hub explains how major AI model evaluation sites differ. Arena reflects human preference, Artificial Analysis helps compare capability, speed, and price, Vals AI focuses on industry tasks, and HELM emphasizes transparency and reproducibility.

Review benchmark sources Select by scenario

Guozhen AI Composite Ranking v0.1

Weighted composite ranking

This is Guozhen AI's original synthesis layer. It normalizes Arena multi-domain preference, Vals real-task evidence, Artificial Analysis production signals, and HELM-style transparency signals into a 0-100 weighted score.

Auto snapshot: 2026-06-01
Refreshes every 3 days; next refresh around 2026-06-05

The automated ranking currently uses the official LMArena Hugging Face Dataset latest snapshots for Text, WebDev, Vision, and Document. Vals, Artificial Analysis, and HELM remain part of the methodology, editorial calibration, and model-selection guidance.

40%

Arena multi-domain preference

Combines Text, WebDev, Vision, Document, and related preference signals from real users.

25%

Vals and real tasks

Uses coding, terminal, industry, and agentic task evidence to avoid chat-only evaluation.

25%

Artificial Analysis

Adds production signals such as intelligence, speed, latency, and price.

10%

HELM and transparent evals

Rewards reproducibility, robustness, multi-metric reporting, and research transparency.

Rank	Model	Composite	Arena	Tasks	Efficiency	Transparency	Best for
1	claude-opus-4-7-thinking Anthropic	94.8	99	94	90	87	Complex reasoning, long documents, engineering agents, WebDev Strong across Arena-style preference and real engineering tasks, making it the strongest composite choice in this snapshot.
2	claude-opus-4-6-thinking Anthropic	93.6	98	92	89	87	Document understanding, deep writing, reasoning-heavy work Very stable across Text, Vision, and Document preference signals, slightly behind the newer thinking model.
3	gemini-3.1-pro-preview Google	91.7	91	96	91	84	Coding, long context, multimodal, search-augmented tasks Strong Vals coding and long-context signals lift its composite score beyond pure chat preference ranking.
4	gpt-5.5-high OpenAI	90.9	88	95	96	83	General intelligence, code repair, production API selection Strong on SWE-style tasks and general intelligence signals, with favorable production trade-offs.
5	claude-opus-4-7 Anthropic	89.4	96	88	86	86	Writing, chat, documents, lighter agent workflows Still very strong in Arena and WebDev, but slightly less reliable than the thinking variant for complex tasks.
6	claude-opus-4-6 Anthropic	88.8	95	87	86	86	Text creation, visual understanding, document analysis A stable all-round model for high-quality content and complex material analysis.
7	gemini-3-pro Google	88.3	90	89	91	84	Vision, multimodal, long context Strong vision and multimodal performance keep it high among Google models.
8	gpt-5.4-high OpenAI	84.1	87	88	85	82	Competitive coding, stable API use, general assistant tasks Still strong in selected academic and coding tasks, but trails GPT-5.5 and newer Claude models overall.
9	qwen3.7-max-20260517 Alibaba	83.7	86	83	86	79	Chinese tasks, WebDev, cost-sensitive API use Notable WebDev performance, with extra value for Chinese-language and cost-aware use cases.
10	gemini-3.5-flash Google	82.6	84	81	93	80	Low latency, multimodal, high-throughput workloads Not the strongest intelligence model, but speed and cost make it useful at production scale.
11	claude-sonnet-4-6 Anthropic	80.8	82	81	82	84	Daily writing, code explanation, cost-controlled tasks Below the Opus tier, but balanced for quality and cost.
12	glm-5.1 Zhipu AI	79.2	82	78	82	75	Chinese Q&A, domestic ecosystem, enterprise private deployment review Good WebDev signal, worth further testing for Chinese and domestic ecosystem scenarios.
13	kimi-k2.6 Moonshot AI	78.4	81	77	82	74	Chinese long documents, knowledge organization, cost-aware workflows Interesting for Chinese long-document work, though cross-source coverage is less complete than top labs.
14	muse-spark Meta	77.1	85	73	78	76	General chat and open-ecosystem tracking Strong Text preference signal, but weaker cross-source task and production coverage lowers the composite rank.
15	deepseek-r1-202605 DeepSeek	76.4	78	79	83	72	Chinese reasoning, math, cost-sensitive API use Good reasoning and cost signals, worth testing for Chinese technical Q&A and budget-sensitive work.
16	deepseek-v3.1 DeepSeek	75.8	77	76	86	72	General Chinese tasks, batch processing, tool use Efficient and cost-aware, useful as a candidate for batch workflows.
17	llama-4-maverick Meta	74.9	75	74	78	88	Open ecosystem, local deployment, research reproducibility Strong openness and transparency, though top task capability trails frontier closed models.
18	qwen3.7-plus Alibaba	74.2	76	73	84	76	Chinese apps, low-cost production, domestic ecosystem Good Chinese ecosystem and cost profile, useful as an enterprise fallback.
19	grok-4 xAI	73.6	76	72	77	70	Fresh information, creative Q&A, social context Interesting for freshness and creative Q&A, with less cross-source coverage than top labs.
20	mistral-large-2 Mistral	72.8	73	72	80	79	EU compliance, open ecosystem, multilingual tasks Useful for multilingual and compliance-sensitive work, though not a top composite performer.

Data update note: Live benchmark fetch failed; using static editorial fallback.; LMArena Text Style Control: fetch failed; LMArena WebDev: fetch failed; LMArena Vision Style Control: fetch failed; LMArena Document Style Control: fetch failed

Trusted Sources

How to read major model benchmark sites

Arena / LMArena

Human preference

Source

Uses anonymous pairwise voting from real users. It is useful for general chat, writing, and preference-driven quality, but a single score should not be treated as the best choice for every workflow.

General chatWriting qualityMultimodal preferenceFrontier tracking

Limitation: Preference data can be affected by sampling, traffic allocation, prompt mix, and model exposure.

Artificial Analysis

Capability, speed, and cost

Source

Tracks intelligence, throughput, latency, and pricing, making it useful for API selection, cost control, and production trade-off analysis.

API selectionCost comparisonLatency and speedGeneral capability

Limitation: Composite scores cannot represent every private workflow; teams still need task-specific evaluation.

Vals AI

Industry task evaluation

Source

Focuses on high-value industry tasks such as finance, law, healthcare, coding, and education, with attention to documents, long context, and agentic workflows.

Finance and lawIndustry documentsLong contextAgent workflows

Limitation: Some datasets and judging details are private, so it is best used as an industry signal rather than a fully reproducible experiment.

Stanford HELM

Transparent reproducible evaluation

Source

Emphasizes transparent scenarios, metrics, and reproducible evaluation, which helps research-minded readers inspect model capability and robustness.

Research reproducibilityCapability breakdownsEvaluation methodsMulti-metric analysis

Limitation: Updates may be slower than commercial leaderboards, so the newest models can lag behind.

Guozhen AI Scorecard

A practical synthesis framework

30%

General intelligence

Compare reasoning, science, math, knowledge, and instruction following instead of trusting one top-ranked model.

25%

Real tasks

Prefer evidence from documents, codebases, tool use, multi-turn workflows, and long context over exam-only scores.

20%

Reliability

Check hallucination risk, format consistency, and whether the model stays coherent across long tasks.

15%

Cost and speed

For similar quality, compare input and output price, latency, throughput, and context window.

10%

Openness and control

Separate closed APIs, open weights, local deployment, compliance, and auditability.

Model Selection

Choose models by real scenario

Writing, Q&A, and knowledge organization

Start with Arena-style preference data, then check Artificial Analysis for speed and cost.

Coding, debugging, and engineering agents

Use LiveCodeBench, SWE-bench, Terminal-Bench, Vals coding tasks, and your own repository tests.

Finance, law, healthcare, and education

Prioritize industry-task benchmarks such as Vals, then add private internal evaluations.

Research and model capability analysis

Use HELM, GPQA, MMLU-Pro, HLE, and methodology notes from benchmark authors.

Local deployment, private data, and compliance

Compare open weights, licenses, deployment cost, context windows, and data retention policy.

Writing, Q&A, and knowledge organization

Start with Arena preference data, then add Artificial Analysis speed and cost signals.

Weights: Arena Text/Document preference 50%, general intelligence 20%, speed and cost 20%, knowledge organization 10%.

claude-opus-4-7-thinking

Anthropic

96.2

Best overall for high-quality writing, long-answer structure, complex Q&A, and document summaries.

claude-opus-4-6-thinking

Anthropic

95.1

Very stable in Text and Document signals, especially for long documents and deep writing.

gemini-3.1-pro-preview

Google

91.8

Strong multimodal, long-context, and information organization ability.

gpt-5.5-high

OpenAI

90.7

Good structured output, production API fit, and general Q&A performance.

gemini-3.5-flash

Google

84.4

Not the highest quality, but useful for fast summarization, rewriting, and lightweight Q&A.

claude-opus-4-7

Anthropic

88.9

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

gemini-3-pro

Google

87.6

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

gpt-5.4-high

OpenAI

86.8

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

claude-sonnet-4-6

Anthropic

85.7

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#10

qwen3.7-max-20260517

Alibaba

84.9

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#11

deepseek-r1-202605

DeepSeek

83.8

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#12

kimi-k2.6

Moonshot AI

82.7

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#13

glm-5.1

Zhipu AI

81.9

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#14

deepseek-v3.1

DeepSeek

81.3

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#15

qwen3.7-plus

Alibaba

80.6

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#16

muse-spark

Meta

79.8

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#17

llama-4-maverick

Meta

78.9

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#18

grok-4

xAI

78.1

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#19

mistral-large-2

Mistral

76.8

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

#20

command-r-plus-next

Cohere

75.9

Extended candidate for writing, Q&A, and knowledge organization; verify with your own prompts before production use.

Coding, debugging, and engineering agents

Prioritize LiveCodeBench, SWE-bench, Terminal-Bench, Vals coding tasks, and your own repository tests.

Weights: Vals/SWE real tasks 40%, WebDev/Arena engineering preference 25%, agent reliability 20%, speed and cost 15%.

gemini-3.1-pro-preview

Google

96.0

Strong coding, long-context, and repository-level understanding signals.

gpt-5.5-high

OpenAI

95.2

Strong SWE-style repair, tool use, and production API behavior.

claude-opus-4-7-thinking

Anthropic

94.5

Excellent WebDev and reasoning signal for frontend refactors and architecture analysis.

qwen3.7-max-20260517

Alibaba

87.6

Notable WebDev signal and worth testing for Chinese engineering workflows.

claude-sonnet-4-6

Anthropic

84.9

Balanced for code explanation, local fixes, and lighter agent workflows.

claude-opus-4-6-thinking

Anthropic

84.0