LLM Leaderboard: Best AI Models Ranked (April 2026)

TL;DR: There is no single best model in April 2026 — the leaderboard has fractured by task. Claude Opus 4.7 leads coding on SWE-bench Verified (82.0%) and tops LM Arena at 1504 Elo. Claude 4.7, Gemini 3.1 Pro Preview, and GPT-5.4 tie on the Artificial Analysis Intelligence Index at 57. DeepSeek V3.2 leads price-performance at $0.29/M input tokens. If you want one API key that covers all of them, ofox.ai routes to every model on this list.

How These Rankings Work

Three independent leaderboards measure different things, and they disagree — which is the point.

LM Arena (formerly LMSYS Chatbot Arena) uses blind human preference votes. Two models answer the same prompt; users pick the better response without knowing which model is which. With 5.7M+ votes across 339 models as of April 2026, it’s the largest human-preference dataset in existence. Scores are Elo ratings — the same system used in chess.

SWE-bench Verified measures whether a model can resolve real GitHub issues. An agent gets a repo, a bug report, and a test suite. It passes if the tests go green. No partial credit. This is the benchmark that actually predicts whether a model will be useful in a coding agent.

GPQA Diamond tests graduate-level science questions in biology, physics, and chemistry — questions designed to be “Google-proof.” Human PhD experts score around 65-70%. Models above 85% are doing something that most domain experts cannot.

Artificial Analysis Intelligence Index aggregates multiple benchmarks into a single composite score, which is useful for a quick overall comparison.

The Overall Leaderboard

The LM Arena top 10 as of April 2026 (source):

Anthropic holds four of the top five spots. The gap between #1 and #10 is 24 Elo points — statistically meaningful but not a blowout. In practice, the top six models are close enough that task fit matters more than raw ranking.

On the Artificial Analysis Intelligence Index, Claude Opus 4.7, Gemini 3.1 Pro Preview, and GPT-5.4 are tied at 57 points. Claude Opus 4.6 scores 53. Kimi K2.6 scores 54 — the strongest open-weight model in the index and ahead of the previous Claude generation.

Best for Coding: SWE-bench Rankings

Claude Opus 4.7 leads SWE-bench Verified at 82.0%, making it the strongest model for autonomous software engineering tasks.

Source: vals.ai SWE-bench leaderboard, verified April 16, 2026.

The spread between #1 and #5 is roughly 4 percentage points. For most coding agent workflows, that gap will show up in edge cases — complex multi-file refactors, ambiguous specs, long-running tasks — rather than simple completions.

Kimi K2.6 is not yet independently listed on SWE-bench Verified. Moonshot AI has published internal numbers that place it in the same band as GPT-5.4 and Gemini 3.1 Pro Preview, but until it shows up on vals.ai or the official swebench.com leaderboard, those numbers should be treated as vendor-reported rather than third-party verified. The related SWE-bench Pro figure of 58.6% is independently confirmed but measures a different, harder benchmark and cannot be compared directly to the Verified scores above.

For a deeper look at Claude’s coding performance, the Claude Opus 4.7 review covers the jump from 4.6 and what changed architecturally.

Best for Reasoning: Composite Intelligence Index

GPQA Diamond is a useful benchmark but the per-model scores reported across vendor blogs and aggregator sites diverge by several percentage points, and an up-to-date independent leaderboard for April 2026 is not publicly available. Rather than reprint numbers we cannot trace to a single authoritative source, we’ll use the Artificial Analysis Intelligence Index — which bundles GPQA Diamond together with MMLU-Pro, AIME, LiveCodeBench, and several other reasoning benchmarks into one composite score.

Source: Artificial Analysis leaderboard, April 2026.

A three-way tie at 57 is more interesting than a single winner. It says the current frontier is a plateau: Claude, Gemini, and GPT all land in the same band across a broad suite of reasoning tasks, and picking between them on “reasoning” alone is a coin flip. The tiebreakers are cost, context window, and task fit — the topics in the next two sections.

For a hands-on look at Gemini 3.1 Pro’s reasoning and its 1M-token context window, see the Gemini 3.1 Pro API guide.

Best Value: Price-Performance

The frontier tier has a 17× price spread on input tokens — and the cheapest option is not far behind the most expensive on the Intelligence Index.

Pricing sourced from ofox.ai/en/models, April 2026. SWE-bench Verified scores from vals.ai.

DeepSeek V3.2 at $0.29/M input is 17× cheaper than Claude Opus 4.7 on input tokens. For high-volume workloads where you’re not pushing the absolute ceiling of coding or reasoning, it’s the obvious starting point. The DeepSeek API pricing guide covers the cache discount that drops input cost to $0.028/M on repeated context.

Kimi K2.6 is the interesting value play at $0.60/M input — roughly 8× cheaper than Claude Opus 4.7 for an Intelligence Index score (54) that sits only 3 points below the frontier band (57). Its SWE-bench Verified rank is still pending independent confirmation, but on broader reasoning benchmarks it’s already competitive.

Best Open-Source Model

Kimi K2.6 from Moonshot AI is the strongest open-weight model currently available, and it’s not close.

It scores 54 on the Artificial Analysis Intelligence Index — ahead of Claude Opus 4.6 (53) and within 3 points of the closed-source frontier band at 57. Moonshot describes it as a 1-trillion-parameter Mixture-of-Experts architecture with 32B active parameters per forward pass, a 256K context window, and API pricing of $0.60/M input and $2.50/M output. It appears on the LM Arena leaderboard and on Artificial Analysis; an independent SWE-bench Verified listing has not been published yet.

For teams that need to self-host, audit model weights, or build on top of a modifiable base, it’s the first open-weight model that belongs in the same conversation as the closed-source frontier.

Which Model Should You Use?

Pick by task, not by headline ranking.

Coding agents: Claude Opus 4.7 (82.0% SWE-bench Verified) is the clearest leader. Cost-sensitive teams should look at Kimi K2.6 first — it costs roughly 8× less and sits 3 points behind the frontier band on the Intelligence Index.

Long-context analysis and research: Gemini 3.1 Pro Preview. It ties for the top band on the Intelligence Index, and its 1M-token context window is the most practical of the frontier models for large-document workflows.

High-volume production: DeepSeek V3.2 at $0.29/M input. Not at the frontier on benchmarks, but close enough for most tasks at a fraction of the cost.

General chat and instruction following: Claude Opus 4.7 (thinking mode) leads LM Arena at 1504 Elo, but the gap to Gemini 3.1 Pro (1493) and GPT-5.4-high (1482) is small enough that it won’t matter for most use cases.

Self-hosted: Kimi K2.6. It’s the only open-weight model that belongs in this conversation.

The full model comparison guide goes deeper on use-case-specific recommendations across Claude, GPT, and Gemini.

Access All These Models Through One API

Every model in this leaderboard — Claude Opus 4.7, Gemini 3.1 Pro, GPT-5.4, DeepSeek V3.2, Kimi K2.6 — is available through ofox.ai with a single API key and OpenAI-compatible endpoints.

One key, one billing account, one SDK integration. Switch models by changing one string:

client = OpenAI(
    base_url="https://api.ofox.ai/v1",
    api_key="your-ofox-key"
)

# Switch between any model on this leaderboard
response = client.chat.completions.create(
    model="anthropic/claude-opus-4.7",  # or google/gemini-3.1-pro-preview
    messages=[{"role": "user", "content": "..."}]
)

For a complete setup guide, see AI API aggregation: access every model through one endpoint.