The 8 Companies Behind Every AI Model You’ll Use in 2026 — Compared

On June 9, Anthropic shipped Claude Fable 5 and invented a tier above its own flagship — twelve days after shipping that flagship. OpenAI's answer is a mini model that outscores half the frontier at a fifteenth of the price. DeepSeek is serving 1M-token context for $0.14 per million tokens, less than some providers charge for cache reads. If you locked in a model provider in January and haven't re-checked the board since, your assumptions are stale.

This is a buyer's comparison of the eight companies that ship the models that matter in mid-2026. Not the models themselves — the companies: their pricing logic, their deployment posture, and what betting on each one actually commits you to. We track all eight in our AI model catalog, where our six-persona panel scores every release; those scores appear here as supporting evidence, with links if you want the full workups.

How to read this comparison

Three axes separate these eight companies more than any benchmark chart:

• Capability ceiling — the best score the provider's top model earns, because that's what you escalate to when a task is hard.
• Unit economics — what the provider's best price-to-capability model costs, because that's what handles your volume.
• Deployment control — open weights or API-only, because that decides whether you can ever leave.

A provider that wins one axis usually concedes another. Anthropic owns the ceiling and charges for it. DeepSeek owns unit economics and asks you to accept a Chinese-origin provider or self-host. Nobody wins all three, which is why most serious teams now run two providers, not one.

The eight at a glance

Provider	Models tracked	Top model (panel score)	Cheapest input $/Mtok	Open weights
Anthropic	9	Claude Fable 5 (9.5)	$1.00	No
OpenAI	9	GPT-5.4 mini (9.0)	$0.05	No
Google	6	Gemini 3.1 Pro (8.7)	$0.10	No
DeepSeek	6	DeepSeek V4-Flash (8.7)	$0.14	Yes (MIT)
Mistral AI	10	Mistral Small 4 (8.5)	$0.10	Yes
Alibaba Cloud	8	Qwen3-235B-A22B (8.5)	$0.06	Yes
Meta	6	Llama 4 Maverick (7.7)	$0.05	Yes
xAI	4	Grok 4.3 (7.7)	$1.00	No

Panel scores are out of 10, from our six-persona review panel. Prices are per million input tokens for each provider's cheapest current model.

The pricing mechanics that quietly re-rank this table

Sticker price per million tokens is the number everyone quotes and almost nobody pays. Three mechanics move the real bill, and providers differ on all three.

Cache pricing is the big one for agentic workloads, where the same system prompt and context get re-sent hundreds of times. Anthropic charges $1.00 for cached input on Fable 5 against $10 fresh — a 90% discount that completely changes the economics of long-running agents. Google prices Gemini 3.1 Pro cache reads at $0.20 against $2.00 fresh, the same ratio. If your workload is cache-heavy, the gap between a $10 model and a $2 model collapses to the gap between $1 and $0.20.

Batch tiers typically halve the price for anything that can wait a few hours — and roughly 40% of the models in our catalog don't publish batch pricing at all, which usually means the provider doesn't offer it. If overnight processing is your pattern, that absence is disqualifying regardless of the headline rate.

Free tiers and launch windows are evaluation subsidies, not pricing. Fable 5's free-plan window closes June 22; DeepSeek's preview pricing has no announced end date, which is its own kind of risk. Budget against the rate card, evaluate on the subsidy.

The closed frontier: Anthropic, OpenAI, Google

Anthropic — the ceiling, priced like it

Anthropic's pitch in June 2026 is blunt: the hardest work goes to the model that can do it, and that model costs $10/$50 per million tokens. Fable 5 posted a SWE-bench Pro of 80.3% against 69.2% for its own two-week-old Opus 4.8 — an 11-point single-generation jump on the benchmark that most resembles real engineering work. The catch is everything around the model: mandatory 30-day data retention, a silent safety fallback that can route under 5% of sessions to Opus 4.8 without telling you, and a value score of 6.5 from our panel — the lowest among frontier leaders, because capability-per-dollar is not the pitch.

Pick Anthropic if your bottleneck is task difficulty, not token budget — long-horizon agent runs, gnarly refactors, work you'd otherwise give a senior engineer. Route the volume elsewhere, even within the Claude family itself.

OpenAI — the deepest bench

OpenAI's strength right now is not its flagship. GPT-5.5 is a fine model that trails Fable 5 badly on agentic coding (58.6 vs 80.3 on SWE-bench Pro). The strength is the lineup beneath it: GPT-5.4 mini scores 9.0 with our panel — matching or beating most flagships — at $0.75/$4.50. That is the best price-to-capability ratio in any closed lineup, and it's why OpenAI keeps winning the volume tier even while losing the ceiling. The risk is churn: OpenAI deprecates models on a cadence that has burned enterprise integrations before, and nine tracked models means nine deprecation clocks.

Pick OpenAI if you want one vendor to cover chat, tools, and bulk processing acceptably well, and you have the engineering discipline to handle model migrations on someone else's schedule.

Google — the infrastructure play

Gemini 3.1 Pro (8.7, $2/$12) is the quiet workhorse of this tier: 1M-token context, strong multimodal coverage, and the fastest output speed among frontier models at ~143 tokens/second. Google's real argument isn't the model — it's that the model lives inside Vertex AI next to your data, your IAM, and your existing GCP bill. For organizations already on Google Cloud, the integration cost of choosing Gemini rounds to zero.

Pick Google if you're a GCP shop, you need long-context multimodal at mid-tier prices, or procurement friction is your binding constraint.

The open-weights bloc: DeepSeek, Alibaba, Mistral, Meta

DeepSeek — the price floor

DeepSeek V4-Flash is the most disruptive single artifact in this market: 8.7 panel score — tied with Gemini 3.1 Pro and GPT-5.5 — at $0.14/$0.28, with MIT-licensed weights and a 1M context window. Our panel gave it a 9.9 value score, the highest in the catalog. The trade-offs are real: preview status with no GA date, a Chinese-origin provider that some threat models exclude outright, and the operational question of whether the API tier survives its own pricing.

Pick DeepSeek if per-token cost is your binding constraint and your compliance posture permits it — or self-host the weights and remove the provider question entirely.

Alibaba Cloud — the open-weights all-rounder

The Qwen line is the broadest open-weights family in our catalog: eight tracked models from edge-sized to Qwen3-235B-A22B (8.5 panel score, 9.5 value). Qwen's coder variants punch above their weight, and the licensing is friendlier than Meta's. Same origin caveat as DeepSeek applies for API use; the weights don't care.

Pick Alibaba/Qwen if you want one open family that scales from a laptop to a cluster, especially for coding workloads.

Mistral AI — the European answer

Ten tracked models — the most of any provider — anchored by Mistral Small 4 (8.5, $0.15/$0.60, open weights). Mistral's differentiation is jurisdictional as much as technical: EU-headquartered, EU-hosted options, and a lineup that covers code (Codestral), reasoning (Magistral), and edge (Ministral). Ceilings are lower than the US frontier; that's the price of the passport.

Pick Mistral if EU data residency or AI Act positioning matters to your legal team, and your workload fits a strong mid-tier model.

Meta — the self-hosting default

Llama 4 Maverick (7.7 panel, 9.0 value) is rarely the best model for a task. It is very often the best model you can own. The Llama license, ecosystem maturity, and inference-stack support make it the default starting point for any team whose first requirement is "no vendor in the loop." The 7.7 ceiling is the cost of sovereignty.

Pick Meta if the deployment requirement is non-negotiable self-hosting and your tasks live comfortably below the frontier.

The wildcard: xAI

xAI — fast iteration, narrow moat

Grok 4.3 (7.7, $1.25/$2.50) ships with real-time X data access and an aggressive release cadence — four tracked models in under a year. The pitch is freshness: live information and agentic search at frontier-adjacent prices. The risk is that everything xAI does well, a bigger provider can bolt on; the live-data moat is the bet.

Pick xAI if your workload genuinely needs real-time social and news signal, not just a recent knowledge cutoff.

Three buying scenarios, resolved

• A 20-person product team shipping an AI feature: OpenAI for volume (GPT-5.4 mini), Anthropic for the hard 5% (route by task difficulty). This is the boring, correct answer in 2026.
• An enterprise with a compliance veto on Chinese providers and a cloud commit: Google or Anthropic on the committed cloud, Mistral as the EU hedge. Skip the price-floor temptation; your lawyers already did.
• An infrastructure team that owns GPUs: Llama 4 or Qwen3 as the base layer, one closed-frontier API key for escalation. Run the math on utilization before assuming self-hosting is cheaper — it usually isn't below ~60% sustained load.
• A solo developer or early-stage startup: start with GPT-5.4 mini or DeepSeek V4-Flash and don't think about it again until you have either a compliance requirement or a task they fail. Provider strategy is a problem you earn.

If you want to pressure-test any of these pairings, our side-by-side compare tool puts pricing, benchmarks, and panel scores in one table.

What to watch through Q3

Three things can re-sort this board again before October. Gemini 3.5 Pro is expected to hit GA and could retake the ceiling conversation. The Fable 5 free window closes June 22, at which point we learn what its real demand curve looks like. And DeepSeek's preview pricing either survives contact with scale or it doesn't — the answer reprices the entire open-weights bloc.

There's a fourth, slower variable: deprecation behavior. Watch how each provider retires what it ships — OpenAI has three models hitting end-of-life in October, while Meta and the open-weights bloc never take a model away from you at all. Over a two-year horizon, that difference costs more engineering time than any per-token delta on this page.

Provider choice in 2026 isn't a marriage. It's a portfolio with quarterly rebalancing. Treat it that way, and the June surprises work for you instead of against you.