Language models compared: subscriptions, capabilities and context

Cohere: expert take

Rerank arithmetic is where the bill surprises people: one search counts as a single request with up to a hundred documents, but any document over five hundred tokens is split, and each piece then counts as its own document.

Where Cohere holds up

• Command A carries enterprise RAG and tool use, tuned for grounded answers over your own data rather than open chat
• State-of-the-art multilingual embeddings across 100+ languages, which most rivals do not match at that spread
• A dedicated Rerank endpoint that measurably lifts search relevance instead of leaning on the LLM to sort results
• Built for private deployment: your own cloud, Azure private hosting, or on-prem, so data stays put
• API-first with SDKs for Python, Go, Node, and Java, so it drops into an existing backend cleanly

★★★★★4.6CE scoreG2 4.5 · 6 reviewsCapterra 4.4Founded 2019Verified July 8, 2026

Google Gemini: expert take

The tier gates credits rather than model access: a hundred a month on free, a thousand in the middle, twenty-five thousand at the top. Part of the same payment buys Google One storage shared with mail and photos, so some of it is gigabytes.

Where Google Gemini holds up

• Answers land inside Gmail, Docs, and Sheets, so drafting and summarizing happen without leaving the file
• Live Google Search grounding means current information instead of a stale training cutoff
• Takes text, images, audio, and video as input, and the ~1M context window swallows long documents whole
• The free tier runs a capable current model with a real monthly credit allowance, not a teaser
• AI Plus at $7.99 is one of the cheaper paid AI tiers going, a soft on-ramp before the $19.99 Pro jump

★★★★★4.5CE scoreG2 4.4 · 22 reviewsCapterra 4.2Founded 2023Verified July 16, 2026

OpenAI API: expert take

Pricing comes in layers rather than tiers: batch and flex run about half of standard, priority costs more, and endpoints with regional data handling add a tenth on the newer models. The same models bought through a marketplace bill on someone else's terms.

Where OpenAI API holds up

• One API covers text, vision, audio in and out, embeddings, and native image generation, so you are not stitching four vendors together
• GPT-5.5 for hard reasoning down to nano tiers for high-volume classification, priced across a wide range
• The Batch API takes 50% off every model, and prompt caching cuts repeated context by another 50%
• o3 and o4-mini handle multi-step reasoning tasks that trip up the general chat models
• The largest developer community of any provider, so most integration problems are already solved somewhere

★★★★★4.8CE scoreG2 4.7 · 11 reviewsCapterra 4.8Founded 2020Verified July 8, 2026

Meta AI: expert take

The free tier is where the ads live: the plan card says so outright, and warns it may slow or skip heavy jobs when the service is busy. No public API and no token price exist on the consumer side, so developers get pointed at open weights.

Where Meta AI holds up

• Lives inside WhatsApp, Instagram, and Messenger, so answers arrive in the thread with nothing to install
• Real-time web search built in, pulling current results instead of answering from a training cutoff
• The core assistant stays free, and the paid Meta One tiers are optional rather than a wall in front of basic use
• Fast image generation right in the chat for quick visual ideas
• Runs on Llama 4, so the underlying model is genuinely current rather than a stripped-down consumer engine

★★★★☆4.4CE scoreG2 4.3 · 5,666 reviewsCapterra 4.3Founded 2023Verified July 8, 2026

Qwen 2.5: expert take

The flagship generation has quietly left the vendor's own commercial list, displaced by the next one, and the only live figure for the larger model now comes from a third-party marketplace. The open weights are still there to download.

Where Qwen 2.5 holds up

• Permissive commercial license on every size, so you can fine-tune, quantize, and ship without asking anyone
• Seven model sizes from 0.5B to 72B let you match the model to the hardware instead of paying for capacity you do not use
• Qwen2.5-Coder is one of the strongest budget coding models you can run locally, and Qwen2.5-Math handles step-by-step arithmetic reliably
• 128K context window swallows a full technical manual in one call, which simplifies long-document RAG
• Genuine multilingual depth across 29-plus languages, with Chinese and East Asian performance most Western models cannot match

Founded 2023Verified July 8, 2026

Groq: expert take

Caching costs nothing to enable; the discount is simply a lower rate on hits, about half. Speech recognition charges a minimum of ten seconds per request, so a two-second clip pays for ten, and compound systems stack server-side tool charges on top of model rates.

Where Groq holds up

• LPU inference runs open-weight models at hundreds of tokens per second, several times faster than GPU hosting
• An OpenAI-compatible API means a near drop-in swap: change the base URL, key, and model name
• Serves current open models including Llama 4 Maverick and Scout plus OpenAI's GPT-OSS
• Time-to-first-token under 100ms makes real-time voice and chat feel immediate
• A genuinely usable free tier plus per-token rates among the lowest for hosted inference

Founded 2016Verified July 8, 2026

Anthropic API (Claude): expert take

Caching cuts both ways. A cache hit costs about a tenth of base input, but writing to the cache costs more than the input itself, a quarter more on the short-lived tier and double on the long one. Reused prompts win, one-shot calls do not.

Where Anthropic API (Claude) holds up

• Up to 1M token context on the frontier models, enough to load an entire codebase or contract set in one call
• Opus 4.8 for the hardest reasoning, Sonnet 5 for balance, Haiku 4.5 for cheap volume, priced per workload
• Holds long instructions and output formatting better than most under load, which matters for coding agents
• Constitutional AI training keeps harmful output low, which is why safety-sensitive teams pick it
• Tool use, JSON mode, and a Batch API that halves the rate for offline high-volume jobs

★★★★★4.8CE scoreG2 4.7 · 297 reviewsCapterra 4.6Founded 2021Verified July 8, 2026

DeepSeek: expert take

The flagship is the throttled one: V4-Pro allows five hundred concurrent requests against two and a half thousand on Flash, so the bigger model is the narrower pipe. Cache hits are the deepest discount in this category, roughly fifty times cheaper than a miss.

Where DeepSeek holds up

• V4-Flash at $0.14 per 1M input undercuts the frontier labs by a wide margin, and cache hits cut that ~98% more
• Open weights you can actually fine-tune and self-host, no gatekeeping and no vendor lock-in
• Strong multilingual work, Mandarin especially, where the Western models are weaker
• Chain-of-thought reasoning that shows its steps, which compliance teams can audit line by line
• The web and mobile chat is free, so evaluating the model costs nothing before you touch the API

★★★★★4.7CE scoreG2 4.6 · 14 reviewsCapterra 4.6Founded 2023Verified July 8, 2026

Claude: expert take

Claude names its meter inside a consumer plan, which is rare: Pro runs on a five-hour window worth roughly forty-four thousand tokens, ten to forty prompts. On the enterprise tier the pretence drops and usage bills at standard API rates.

Where Claude holds up

• Industry-leading 1M token context window for deep analysis
• Excels at nuanced writing, summarization, and creative tasks
• Strong constitutional AI framework prioritizes safety and ethics
• Artifacts feature for iterative code generation and editing
• Generous free tier with access to the powerful Sonnet model

★★★★★4.7CE scoreG2 4.6 · 297 reviewsCapterra 4.5Founded 2023Verified July 8, 2026

ChatGPT: expert take

The $7 Go plan is the cheap step that can carry advertising, which the plan card says outright. Above it the ladder stops selling features and starts selling multiples of Plus usage, five times and twenty times, each at its own price.

Where ChatGPT holds up

• Frontier models on tap: GPT-5.5 for hard reasoning, cheaper GPT-5 and mini tiers for volume
• Largest third-party ecosystem, custom GPTs, and connector support of any assistant
• Multimodal in one place: voice, images in and out, file uploads, and web search
• Free tier is genuinely usable, not a teaser
• Simple interface that non-technical people pick up in minutes

★★★★★4.8CE scoreG2 4.7 · 2,268 reviewsCapterra 4.6Founded 2022Verified July 8, 2026

Replicate: expert take

One vendor, two billing currencies: some models bill hardware by the second, others bill input and output tokens, and which one applies is a property of the model rather than your account. Multi-GPU capacity opens only under a spend commitment.

Where Replicate holds up

• Highly rated (4.5/5 on review platforms)
• 12 key features including 50K+ models and Simple API
• Growing user base (200K+)
• API access for custom integrations

★★★★☆4.4CE scoreG2 4.3 · 110 reviewsCapterra 4.4Founded 2021Verified July 8, 2026

Command R+: expert take

The ceiling hides in the limits line: the context window runs to 128k tokens while output stops at four thousand, which quietly caps what a single call can return. Output is priced four times input on that same line.

Where Command R+ holds up

• Grounded RAG with in-line citations, so every answer traces back to a source you can check
• Multi-step tool use for chaining real business workflows, not just single-shot completions
• Ten-language coverage for global enterprise deployments out of the box
• A 128k context window at a mid-tier price that reads long documents without splitting them
• Deployable in cloud, VPC, or on-prem, so regulated data can stay inside your walls

★★★★★4.6CE scoreG2 4.5 · 83 reviewsCapterra 4.2Founded 2024Verified July 8, 2026

Google AI Studio: expert take

Surcharges outnumber discounts on this one. Priority runs about double standard, a prompt past two hundred thousand tokens doubles the rate again on the larger models, and cached context is billed for storage by the hour rather than per hit.

Where Google AI Studio holds up

• Free access to Gemini 3.1 Pro with a context window near 1M tokens, no card required to start prompting
• An API key drops out of the same screen, so a prototype moves to code in minutes
• System instructions pin model behavior without you rebuilding the prompt every call
• Function calling and code execution are built in, so you can test tool use and run Python inline
• Search grounding wires answers to live results, and text, image, audio, and video all go in as input

★★★★☆4.3CE scoreG2 4.2 · 1,028 reviewsCapterra 4.4Founded 2023Verified July 16, 2026

Mistral AI: expert take

The $24.99 Team plan is not the whole team price: a base account fee sits on top of the per-user rate, so the seat figure understates the invoice. Document recognition run in batch costs about half the standard rate.

Where Mistral AI holds up

• Open-weight models you can download and self-host, so data never has to leave your own infrastructure
• An EU base with GDPR-native handling, which clears sovereignty reviews that block US-hosted providers
• A wide model menu, Large 3, Medium 3.5, Small 4, Codestral, Magistral, Pixtral, so you match the model to the job and the budget
• La Plateforme bills pay-as-you-go per token with no subscription requirement, and Small 4 is genuinely cheap to run
• Function calling, JSON mode, and the Agents API make it a real base for structured, tool-using applications

★★★★★4.6CE scoreG2 4.5 · 13 reviewsCapterra 4.4Founded 2023Verified July 16, 2026

Mistral Large: expert take

The $5.99 Mistral Pro (Student) plan is the interesting line: the same consumer product at well under half the standard rate, proof of enrolment required. Twelve months is the cap on it, after which the student rate expires.

Where Mistral Large holds up

• GDPR-native with EU hosting, which clears data-residency and sovereignty reviews that block US-hosted models
• The API bills $2 per 1M input and $6 per 1M output, and batch jobs run at half that, which is gentle for a flagship-class model
• Real multilingual depth across French, German, Spanish, Italian, and Portuguese, not just English with translation bolted on
• Function calling and constrained JSON output make it dependable inside structured, tool-using workflows
• Open-weight Mistral releases sit alongside it, so you can self-host for the workloads that cannot leave your walls

★★★★☆4.4CE scoreG2 4.3 · 13 reviewsCapterra 4.2Founded 2024Verified July 8, 2026

Grok 2: expert take

The personal ladder makes one jump and it is tenfold: $30 SuperGrok to $300 SuperGrok Heavy, with nothing in between. A Business seat costs the same $30 as a personal one, so the organisation pays no premium here.

Where Grok 2 holds up

• Live X data access for genuinely current answers on breaking topics
• Native image generation from the Aurora model, built into the same interface
• An unfiltered, direct conversational tone that suits informal exploration
• Bundled with X Premium at no extra charge for existing subscribers
• Open model weights released for researchers to inspect and build on

★★★★☆4.3CE scoreG2 4.2Capterra 4.1Founded 2024Verified July 8, 2026

Phi-3: expert take

Fine-tuning is billed in three separate layers here: training per million tokens, hosting by the hour, and inference on top of both. The two medium variants cost the same whether you take the short or the long context window.

Where Phi-3 holds up

• Runs efficiently on-device, putting offline AI on phones, IoT hardware, and modest laptops with no cloud call
• MIT license allows commercial use with almost no restrictions, and self-hosting carries no per-token fee
• Beats several larger models on reasoning benchmarks like MMLU and GSM8K for its parameter count
• Quantized builds run on CPU, so you avoid the expensive GPU requirement of bigger models
• A 128K context option on a model this small and cheap to serve, which is unusual at the size

★★★★☆4.1CE scoreG2 4Capterra 4Founded 2024Verified July 8, 2026

Llama (Meta): expert take

The rates attached to this model belong to a third-party host, not to Meta: the first-party API is waitlisted and publishes no price at all. What you actually pay for is your own hardware, which is the only meter that applies.

Where Llama (Meta) holds up

• A permissive license that allows commercial use and modification, not just research
• Strong current-generation performance for open weights across the Llama 4 family
• Full data control and privacy, since the model runs entirely on infrastructure you own
• A huge ecosystem of fine-tuned variants and tooling on Hugging Face to build from
• Multiple parameter sizes, so you can match the model to the hardware you actually have

★★★★★4.7CE scoreG2 4.6 · 152 reviewsCapterra 4.7Founded 2023Verified July 8, 2026