Pillar Guide · May 2026

Best AI Models in May 2026: Closed vs Open-Weight, Tested and Ranked

The AI model landscape in May 2026 is the most crowded it has ever been. Five major closed frontier releases since February (Gemini 3.1 Pro, Claude Sonnet 5, Claude Opus 4.7, GPT-5.5, Grok 4.3). Six major open-weight releases (DeepSeek V4-Pro/Flash, Qwen3-Coder-Next, Qwen3.6-27B, GLM-5, Kimi K2.6, Mistral Medium 3.5). Pricing fell 30-60% across the board. Open-weight quality closed to within 5-15 points of closed frontier.

This is the complete comparison. We've tested every model on the benchmarks engineering teams actually care about — SWE-Bench Verified, LiveCodeBench, MMLU-Pro, ARC-AGI-2, GPQA Diamond, AIME 2025 — and laid out which to pick by workload, hardware budget, and privacy requirements. Numbers are verified against third-party leaderboards (Artificial Analysis, BenchLM, SWE-Bench public leaderboard) where available.

📅 Published: May 9, 2026🔄 Last Updated: May 9, 2026✓ Manually Reviewed

TL;DR — best by category

Best for coding

Claude Sonnet 5 — 92.4% SWE-Bench Verified.

Best for math & reasoning

GPT-5.5 Pro — 95.2% AIME 2025.

Best general reasoning

Gemini 3.1 Pro — 77.1% ARC-AGI-2 + 1M context.

Best self-hostable frontier

DeepSeek V4-Pro — MIT, 1M context, 82.6% SWE-Bench.

Best for agentic coding

Kimi K2.6 — ties GPT-5.5, 5-10× cheaper API.

Best single-GPU local

Qwen3.6-27B — fits in RTX 5090 / RTX 4090.

Best local coding

Qwen3-Coder-Next — 70.6% SWE-Bench, 256K context.

Best dense open-weight

Mistral Medium 3.5 — 128B unified model.

Frontier closed models (API-only)

The four closed frontier models in May 2026: Gemini 3.1 Pro, Claude Sonnet 5, Claude Opus 4.7, and GPT-5.5. None can be self-hosted; all require an API or subscription. Each leads a different category.

Model	Vendor	Context	Pricing per Mtok	Best for
Gemini 3.1 Pro	Google	1,000K	$2 / $12	Whole-codebase analysis, video, ARC-AGI-2 reasoning
Claude Sonnet 5	Anthropic	200K	$3 / $15	Production coding (top SWE-Bench), Cursor/Aider
Claude Opus 4.7	Anthropic	200K	$15 / $75	Hardest reasoning, Adaptive Thinking
GPT-5.5	OpenAI	400K	$5 / $30	Math, ChatGPT ecosystem, plugins

Frontier open-weight models

Three open-weight models reach genuine frontier-class capability in May 2026: DeepSeek V4 (1M context, MIT), Kimi K2.6 (1T MoE, agentic-first), and GLM-5 (745B/44B active). All require serious infrastructure (4-8× H100 minimum) but are the only realistic option for self-hosted frontier-class deployment.

Model	Active params	Context	License	Hardware floor
DeepSeek V4-Pro	49B (1.6T total)	1,000K	MIT	8× H100
DeepSeek V4-Flash	13B (284B total)	1,000K	MIT	2× H100
Kimi K2.6	32B (1T total)	200K	Modified MIT	8× H100
GLM-5	44B (745B total)	200K	MIT	4× H100

Single-GPU open weight (prosumer)

For self-hosters with one good GPU (RTX 5090, RTX 4090, M3 Max/Ultra, or single H100), three open-weight models stand out. All Apache 2.0 or modified MIT.

Model	VRAM (Q4)	SWE-Bench	Best for
Qwen3.6-27B	~17 GB	68.9%	General + coding mix on one GPU
Qwen3-Coder-Next	~52 GB	70.6%	Coding-only, 1× H100 / 2× RTX 5090
Mistral Medium 3.5	~80 GB	77.6%	Unified general/coding/vision

Coding-specialized comparison

SWE-Bench Verified, LiveCodeBench, and Aider polyglot are the three benchmarks engineering teams cite most often. Numbers below are vendor-published, cross-checked against public leaderboards.

Model	SWE-Bench Verified	LiveCodeBench	Aider polyglot
Claude Sonnet 5	92.4%	79.8%	87.1%
Claude Opus 4.7	87.6%	77.2%	85.4%
Gemini 3.1 Pro	87.9%	75.6%	82.7%
GPT-5.5	85.1%	76.3%	81.4%
Kimi K2.6	85.4%	76.8%	82.1%
DeepSeek V4-Pro	82.6%	73.4%	79.3%
DeepSeek V4-Flash	78.4%	67.2%	74.1%
Mistral Medium 3.5	77.6%	71.6%	76.2%
GLM-5	77.8%	71.6%	75.4%
Qwen3-Coder-Next	70.6%	68.4%	71.2%
Qwen3.6-27B	68.9%	66.2%	68.3%

Reasoning & knowledge comparison

Model	MMLU-Pro	GPQA Diamond	ARC-AGI-2	AIME 2025
Gemini 3.1 Pro	89.4%	88.2%	77.1%	94.0%
GPT-5.5	90.1%	86.0%	71.3%	95.2%
Claude Sonnet 5	87.9%	85.7%	68.4%	91.5%
Claude Opus 4.7	89.4%	87.3%	71.8%	92.8%
DeepSeek V4-Pro	86.3%	81.4%	59.8%	88.7%
Kimi K2.6	88.1%	82.7%	62.4%	87.2%
GLM-5	84.6%	79.4%	56.2%	85.2%
Mistral Medium 3.5	85.2%	76.4%	54.8%	81.6%

Context window comparison

For workloads needing whole-codebase or long-document context, only four models offer 1M-token context:

Context	Models
1,000,000 tokens	Gemini 3.1 Pro · DeepSeek V4-Pro · DeepSeek V4-Flash
400,000 tokens	GPT-5.5
256,000 tokens	Qwen3-Coder-Next · Mistral Medium 3.5
200,000 tokens	Claude Sonnet 5 · Claude Opus 4.7 · Kimi K2.6 · GLM-5
128,000 tokens	Qwen3.6-27B

Pricing comparison (closed model APIs)

Model	Input ($/Mtok)	Output ($/Mtok)
GPT-5.5 Instant	$1.50	$6.00
Gemini 3.1 Pro	$2.00	$12.00
Claude Sonnet 5	$3.00	$15.00
GPT-5.5 Standard	$5.00	$30.00
Kimi K2.6 (Moonshot API)	$0.60	$2.50
Claude Opus 4.7	$15.00	$75.00
GPT-5.5 Pro	$15.00	$60.00

Open-weight models have zero per-token cost after hardware investment. A $10-15K multi-GPU rig running DeepSeek V4-Flash or Qwen3-Coder-Next pays for itself in ~12-24 months versus $200-500/month per developer on closed APIs.

How to pick the right model

The decision tree most production teams use, in order:

1. Can your data leave your network?
If no — only open-weight options apply. Skip to Q3.
2. Is workload coding or general?
Coding → Claude Sonnet 5. General with hard reasoning → Gemini 3.1 Pro. Math → GPT-5.5. Mixed → ChatGPT ecosystem dictates.
3. What hardware do you have?
8× H100 → DeepSeek V4-Pro or Kimi K2.6. 4× H100 → GLM-5. 2× H100 / 4× RTX 5090 → DeepSeek V4-Flash. 1× H100 → Qwen3-Coder-Next or Mistral Medium 3.5. Single consumer GPU → Qwen3.6-27B.
4. Do you need 1M context?
Yes → Gemini 3.1 Pro (closed) or DeepSeek V4 (open). No → most other models work.

When to use both: hybrid setups

Most production teams in May 2026 don't pick one model — they run a hybrid stack. The pattern:

✓Local model for routine 70-80% of traffic. Qwen3-Coder-Next, DeepSeek V4-Flash, or Mistral Medium 3.5 handle code completion, simple Q&A, classification, and routine refactors.
✓Closed API for hardest 20-30%. Claude Sonnet 5 for difficult coding, GPT-5.5 for math/research, Gemini 3.1 Pro for whole-codebase analysis.
✓Routing logic. Simple heuristic: prompt tokens > 50K, multi-step reasoning, or evaluation produces low confidence → escalate to API. Otherwise stay local.
✓Cost outcome. Typical 60-85% reduction vs pure-API. For a developer paying $300/month on Claude Sonnet 5, hybrid drops to $50-100 + amortized hardware.

Frequently asked questions

What is the best AI model in May 2026?

There is no single best AI model in May 2026 — different models lead different categories. For coding: Claude Sonnet 5 leads SWE-Bench Verified at 92.4%. For general reasoning: Gemini 3.1 Pro leads ARC-AGI-2 at 77.1%. For math: GPT-5.5 leads AIME 2025 at 95.2%. For self-hostable open weight: DeepSeek V4-Pro is the frontier (1M context, MIT licensed, 82.6% SWE-Bench). For best-on-a-single-GPU: Qwen3-Coder-Next (coding) or Qwen3.6-27B (general). Pick by workload, not by hype.

Should I use a closed model (API) or open-weight (self-hosted)?

Closed (Gemini 3.1 Pro, Claude Sonnet 5, GPT-5.5) wins on absolute peak quality and zero infrastructure overhead. Open-weight (DeepSeek V4, Qwen3-Coder-Next, GLM-5, Mistral Medium 3.5) wins on data privacy, predictable monthly costs, offline operation, and product-IP independence. Most production teams use both: open-weight local for routine 70-80% of traffic, closed API for hardest 20-30%. Typical cost reduction vs pure-API: 60-85%. The right answer depends on your workload, hardware budget, and privacy requirements.

Which open-weight model can I run on a single GPU?

On a single high-end consumer GPU (RTX 5090 32GB or RTX 4090 24GB): Qwen3.6-27B (~17 GB Q4) is the strongest single-GPU option for general work, scoring 68.9% on SWE-Bench Verified — and it actually beats its own much-larger 397B MoE sibling on agentic coding. For coding-only workloads, Qwen3-Coder-Next (~52 GB Q4) needs 1× H100 or 2× RTX 5090 but scores 70.6%. On Apple Silicon (M3 Max/Ultra), Qwen3.6-27B and Mistral Medium 3.5 (Q4) both run cleanly on Metal.

How big is the gap between closed and open-weight in May 2026?

Smaller than ever. On most benchmarks, the best open-weight models land within 5-15 points of the best closed models. SWE-Bench Verified: Sonnet 5 92.4% vs DeepSeek V4-Pro 82.6% (10 points). MMLU-Pro: GPT-5.5 90.1% vs DeepSeek V4-Pro 86.3% (4 points). ARC-AGI-2: Gemini 3.1 Pro 77.1% vs DeepSeek V4-Pro 59.8% (17 points — the biggest gap). The pattern: open weight is now “good enough” for production work; closed models still win the highest-stakes individual tasks.

What changed since 2025?

Five things matter most. 1) Frontier closed models added thinking modes (Gemini 3.1 Pro Tier 1/2/3, GPT-5.5 Instant/Standard/Pro, Claude Opus 4.7 Adaptive Thinking). 2) Open-weight quality jumped — DeepSeek V4 and Kimi K2.6 are within 10% of closed leaders, MIT licensed. 3) Context windows hit 1M (Gemini 3.1 Pro, DeepSeek V4) — whole-monorepo work is practical. 4) Coding scores hit production-useful levels — Sonnet 5 92.4% SWE-Bench Verified means 462/500 real GitHub bugs fixed correctly. 5) Pricing fell — Kimi K2.6 API is 5-10× cheaper than GPT-5.5 for agentic workloads.

Is Llama 4 still relevant in May 2026?

Yes for narrow use cases, no as a default choice. Llama 4 Scout (17B active) and Llama 4 Maverick (still rumored) ship with Meta's modified license that has usage thresholds, attribution requirements, and competitive-use restrictions. Open-weight alternatives like DeepSeek V4 (MIT) and Qwen3 family (Apache 2.0) deliver comparable quality with less restrictive licensing — most production teams have moved to those. Use Llama 4 if you specifically need its multimodal vision tier or you're already integrated with Meta's ecosystem; otherwise Qwen3 / DeepSeek / GLM-5 are simpler.

Which model should I pick for an AI coding agent?

For a Cursor-like coding agent: Claude Sonnet 5 (closed, top SWE-Bench) for production work, with Qwen3-Coder-Next (open weight, ~75% of Sonnet 5 quality) as the local fallback or hybrid layer. For an autonomous bug-fixing agent: Kimi K2.6 (agentic-first training, ties GPT-5.5 on agentic benchmarks at 5-10× lower API cost). For a research agent that needs to load whole monorepos: Gemini 3.1 Pro (1M context) or DeepSeek V4-Pro (1M context, self-hostable). For a privacy-required coding agent: Qwen3-Coder-Next or DeepSeek V4-Flash, both running locally.

Will another major release land in the next 6 months?

Almost certainly. Anthropic typically ships ~quarterly (Sonnet 5 in April, expect another by Q3). OpenAI follows GPT-5 → GPT-5.5 cadence (next bump likely late summer 2026). Google's Gemini 3.1 → 3.5 jump is already telegraphed. On the open-weight side, DeepSeek and Qwen ship aggressively — expect V5 and Qwen 4 by year-end. Kimi K3 is in active development. Expect us to refresh this comparison every 60-90 days as the landscape shifts. Subscribe via the newsletter below for major-release updates.

Build a production hybrid AI stack

Local AI Master's deployment course walks through the full hybrid pattern — running open-weight models with vLLM, integrating closed APIs as fallback, building routing logic, and deploying to production. Real GitHub repo, real code.

See the deployment course →

Ready to Go Beyond Tutorials?

10 structured courses with hands-on chapters - build RAG chatbots, AI agents, and ML pipelines on your own hardware.

Start Learning Free See pricing

Individual model deep dives

Written by Pattanaik Ramswarup

Creator of Local AI Master

I build Local AI Master around practical, testable local AI workflows: model selection, hardware planning, RAG systems, agents, and MLOps. The goal is to turn scattered tutorials into a structured learning path you can follow on your own hardware.

✓ Local AI Curriculum✓ Hands-On Projects✓ Open Source Contributor

GitHub LinkedIn Twitter