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AI Model Comparison (2026)

Every major 2026 LLM side by side — context window, API price per 1M tokens, modality, and whether the weights are open. Filter by provider or open-weights, sort by price or context. Verified and cross-checked; long-context surcharges disclosed.

Want the editorial read — cheapest to run, the open-vs-closed gap? See the Frontier Index →

Swipe for price & modality →

ModelContextIn $/1MOut $/1MWeightsModality
Qwen 3.7 MaxAlibaba · 2026-05Agent-first flagship; strong tool-use + multilingual (esp. Chinese) reasoning1M$2.50*$7.50ProprietaryMultimodal
Claude Fable 5Anthropic · 2026-06-09Anthropic's most capable widely-released model; top-end reasoning + agentic work1M$10$50ProprietaryText + image in
Claude Haiku 4.5Anthropic · 2025-10Fastest Claude with near-frontier intelligence at low cost200K$1$5ProprietaryText + image in
Claude Opus 4.8Anthropic · 2026Most capable Opus-tier model for complex reasoning + long-horizon agentic coding1M$5$25ProprietaryText + image in
Claude Sonnet 4.6Anthropic · 2026Best balance of speed and intelligence; strong agentic + coding workhorse1M$3*$15ProprietaryText + image in
DeepSeek V4DeepSeek · 2026-04-24Open-weights MoE (~32–37B active) frontier value; sparse attention1M$0.43*$0.87OpenMultimodal
Gemini 3.1 ProGoogle · 2026-02-19Frontier long-context multimodal reasoning; largest practical context in class1M$2*$12ProprietaryMultimodal (text/image/audio/video)
Gemini 3.5 FlashGoogle · 2026-05-20Flagship-class fast model; 280+ tok/s, default in Gemini app + Search AI Mode1M$1.50$9ProprietaryMultimodal (text/image/audio/video)
Llama 4 MaverickMeta · 2025-04-05Open-weights MoE; cheapest frontier-ish option, huge ecosystem + fine-tunability1M$0.22*$0.85OpenMultimodal (text + image)
Mistral Large 3Mistral AI · 2025-12-01Open Apache-2.0 European flagship; self-hostable, strong multilingual + coding256K$0.50$1.50OpenText + image
Kimi K2 ThinkingMoonshot AI · 2025-11Open 1T-param MoE (32B active) built for long-horizon agentic reasoning256K$0.60$2.50OpenText (agentic)
GPT-5.4 miniOpenAI · 2026Cost-efficient mid-tier for high-volume tasks with solid reasoning400K$0.75$4.50ProprietaryText + image in
GPT-5.4 nanoOpenAI · 2026Cheapest OpenAI tier for simple, latency-sensitive, high-volume calls400K$0.20$1.25ProprietaryText + image in
GPT-5.5OpenAI · 2026-04-23OpenAI flagship; first OpenAI model with a 1M-token API context window1.05M$5*$30ProprietaryText + image in
GPT-5.6 LunaOpenAI · 2026-07-09Fastest, lowest-cost GPT-5.6 tier for high-volume work1.05M$1*$6ProprietaryText + image in
GPT-5.6 SolOpenAI · 2026-07-09OpenAI's flagship GPT-5.6 tier for complex coding, research, and agentic work1.05M$5*$30ProprietaryText + image in
GPT-5.6 TerraOpenAI · 2026-07-09Balanced GPT-5.6 tier — near-GPT-5.5 quality at about half the price1.05M$2.50*$15ProprietaryText + image in
Grok 4.3xAI · 2026xAI's fastest + most intelligent GA model; native real-time X / web search1M$1.25*$2.50ProprietaryText + image in
GLM-5Zhipu / Z.ai · 2026-02-11Open MIT-licensed frontier model; very strong coding + agentic value200K$1*$3.20OpenMultimodal

Standard pay-as-you-go list prices, USD per 1M tokens, as of 20 Jul 2026 — for prompts up to ~200K tokens. * = tiered/long-context or promo pricing applies (hover the input price). Open-weights API prices are representative third-party hosted rates; self-hosting is free. This space moves weekly — confirm with each provider before relying on a figure.

Frequently asked

Which 2026 model has the cheapest API pricing?

Among frontier-class options, third-party-hosted open-weights models are cheapest — Llama 4 Maverick runs about $0.22 / $0.85 per 1M tokens (input / output). Among major proprietary APIs, OpenAI's GPT-5.4 nano ($0.2 / $1.25) and Google's smaller Flash tiers are the lowest. DeepSeek V4 is unusually cheap for its capability, often discounted well below its $0.435 / $0.87 list rate.

Which model has the largest context window?

By the verified API context windows in this table, GPT-5.5 leads at ~1.05M tokens, just ahead of a ~1M cluster — Claude Opus 4.8 / Claude Sonnet 4.6 / Claude Fable 5, Gemini 3.1 Pro, Grok 4.3, and the open-weights DeepSeek V4 and Llama 4 Maverick. Google and Meta have publicly discussed larger windows (a Gemini tier up to ~2M, a Llama 4 Scout variant up to 10M), but those aren't reflected in these standard API rows.

What are the best open-weights models in mid-2026?

The strongest open-weights options are DeepSeek V4 (Apache-2.0), Meta's Llama 4 Maverick, Moonshot's Kimi K2 Thinking, Zhipu's MIT-licensed GLM-5, and Mistral Large 3 (Apache-2.0). Several rival proprietary frontier models on coding and agentic tasks, and all can be self-hosted or run via many providers.

Why are some prices shown with an asterisk and a footnote?

Several flagships use context-tiered pricing: the listed figure is the rate for prompts up to ~200K tokens, and the footnote shows the higher long-context rate. For example, Gemini 3.1 Pro lists $2 / $12 at the base tier but >200K billed $4/$18 (long-context tier); GPT-5.5 charges more on long prompts (>272K input billed 2x input / 1.5x output). Claude Opus 4.8, by contrast, has no long-context premium.

Is Grok 5 available yet?

Not as a generally available API as of June 2026. Grok 5 has been widely discussed (rumored ~6T params, 1.5M context) but xAI had not published an official release or pricing at the time of writing, so this table uses Grok 4.3, the current GA flagship.

How to compare AI models

Comparing models is not about a single leaderboard number. The useful comparison lines up the things that actually affect your use: price per token, context-window size, speed, modality, and whether the weights are open.

Price is two numbers, not one

Input and output tokens are usually priced differently, so a model that looks cheap on input can be expensive if your workload generates long outputs. Compare a blended rate that reflects your own input-to-output mix, not just the headline input price.

Context and modality matter

A larger context window lets a model handle longer documents in one pass; multimodal support lets it read images or audio. A cheaper model with the right context or modality often beats a pricier one that lacks it.

Open versus closed

Open-weight models can be self-hosted for data control and zero per-token cost; closed API models are simplest and often the most capable. All figures here come from the hand-verified Frontier Ledger, so they stay consistent across the site.

Related reading

Go deeper on how this works and what to pick.

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