Qodo Review 2026

GitHub Copilot

Featured

Now billed with AI Credits, since the June 2026 overhaul

GitHub Copilot suggests code and entire functions in real time inside your editor, powered by a selection of AI models and trained on a massive corpus of code. A structural billing change took effect June 1, 2026: GitHub replaced its older Premium Request Units system with GitHub AI Credits, a usage-based model where 1 credit equals $0.01 — plan prices stayed the same, but what they actually cover changed fundamentally. Code completions and inline suggestions remain unlimited and free on every paid plan, unaffected by the credit system. Chat, agent mode, code review and other AI-heavy features now draw from a monthly credit pool that refreshes each billing cycle: Free includes 2,000 code completions plus limited chat/agent access on GPT-5 mini and Claude Haiku 4.5; Pro is $10/month with $15 in monthly credits ($10 base plus a $5 variable flex allotment); Pro+ is $39/month with $70 in credits; and Max, a new top tier, is $100/month with $200 in credits for sustained heavy agentic use. Business runs $19/seat/month and Enterprise $39/seat/month, both drawing from organization-pooled credits (GitHub currently directs organizations to sales for current per-seat allotments), with Business and Enterprise customers receiving 2x promotional credits through August 2026 during the transition. IP indemnification covers unmodified Copilot suggestions on Business and Enterprise when duplication detection is enabled. As of April 22, 2026, GitHub temporarily paused new self-serve Business sign-ups for organizations on Free/Team GitHub plans (paid GitHub plan orgs are unaffected). For developers who mainly use inline completions, the June 2026 change is largely invisible; for heavy chat and agent-mode users, credit consumption is now the real thing to budget against, not just the flat plan price.

Tabnine

Privacy-first AI code completion with zero code retention

Tabnine predates GitHub Copilot — it launched in 2018 as one of the first AI code completion tools — and has built its entire identity around privacy and deployment control rather than trying to out-feature Cursor or Windsurf on agentic capability. Its core promise is zero code retention (your code is never stored or used to train shared models) plus genuinely flexible deployment: fully SaaS, VPC, on-premises, or completely air-gapped for organizations that can't send code outside their own network under any circumstances. A meaningful change worth knowing: Tabnine retired its free tier entirely in 2024. Where it once offered a generous free plan that made it a common recommendation for students and budget-conscious developers, it's now a paid-from-day-one product. The Code Assistant plan runs $39/user/month (annual billing) for completions, IDE-integrated chat, IP protection and flexible deployment; the newer Agentic Platform tier adds autonomous multi-step agents, MCP tool support and a CLI for $59/user/month. At those prices, Tabnine sits well above Cursor Pro ($20/month) and GitHub Copilot Pro ($10/month) for comparable core completion quality — the premium is specifically for the privacy guarantees and deployment flexibility, not raw AI capability. It's the right choice for security-conscious enterprises, defense contractors, and regulated industries that genuinely need air-gapped deployment or contractual zero-retention guarantees; for a solo developer or small team without those specific requirements, a cheaper competitor will likely deliver similar day-to-day value.

Cody by Sourcegraph

Now enterprise-only — individuals should look at Amp instead

Cody's story took a sharp turn in mid-2025 that anyone researching it in 2026 needs to know upfront: Sourcegraph discontinued the Cody Free and Cody Pro plans entirely on July 23, 2025, redirecting individual and small-team users to a new, separate agentic tool called Amp (which itself spun off into its own company in December 2025). If you're an individual developer who used to run Cody Free or Pro, that product no longer exists for you — Cody is now sold exclusively as part of Sourcegraph Enterprise. What remains is Cody Enterprise, bundled into Sourcegraph's broader code-intelligence platform starting around $16,000 (or $59/user/month on some pricing pages, annual contract, sales-led). Its genuine differentiator — and the reason large organizations still pay for it — is deep cross-repository context: Sourcegraph's code search engine indexes potentially billions of lines across an entire organization's repositories, letting Cody answer questions like "which services call this API?" across a monorepo or many separate repos simultaneously, something workspace-scoped tools like GitHub Copilot fundamentally can't do. Cody Enterprise includes admin-configurable model choice (Claude, GPT, Gemini), zero-data-retention guarantees, SOC 2 compliance, and cloud, self-hosted or fully air-gapped deployment — and is reportedly used by four of the six largest US banks. But it leans more toward chat, inline edits and autocomplete than fully autonomous multi-file agentic workflows; for that, Sourcegraph now points customers toward Amp instead. Bottom line: if you're an individual or small team, Cody isn't available to you anymore — evaluate Amp, or a competitor like Windsurf or Tabnine instead.

Hugging Face

Trending

The largest open-source AI hub — 800,000+ models, free to browse

Hugging Face is the closest thing the AI industry has to GitHub: a hub hosting over 800,000 open-source models, 100,000+ datasets, and Spaces (hosted demo apps), all free to browse, download and use, with a thriving community driving much of its content. Beyond the hub itself, its Inference offerings split into three distinct products, a naming overlap that trips up newcomers: the Serverless Inference API (free tier with rate limits, best for prototyping), Inference Endpoints (dedicated GPU instances you spin up per model, starting around $0.50-0.60/hour with scale-to-zero), and Inference Providers (a unified OpenAI-compatible gateway routing to 15+ third-party providers including Groq, Together AI, Fireworks and Replicate). Inference Providers is the more recent addition, maturing through 2025-2026 as Hugging Face's answer to "which inference partner should I use" — rather than picking one provider directly, you call Hugging Face's single endpoint and it routes to whichever underlying provider hosts the model, with pricing passed through at each provider's own published rate. Free users get a small monthly credit allocation ($0.10) toward this routed inference, while PRO subscribers ($9/month) get $2.00 in monthly credits plus faster rate limits and private Spaces — a meaningful upgrade for anyone doing more than light experimentation. For teams choosing between Hugging Face and Replicate specifically: Hugging Face wins decisively on sheer model breadth and community size, while Replicate offers a somewhat simpler, more image-generation-optimized API experience. For teams choosing between Hugging Face and LangChain: they're not really competitors — Hugging Face is model hosting and inference, LangChain is an application-building framework — and many production stacks genuinely use both together.

OpenAI API

Trending

Build with GPT-5.6 — pricing that shifts with a genuine AI price war

OpenAI's API gives developers programmatic access to the GPT model family for building chatbots, agents, content generation and any application needing language understanding, billed per token rather than a flat subscription. The current flagship lineup, GPT-5.6, reached general availability on July 9, 2026 across three tiers — Sol (frontier reasoning and complex agent tasks), Terra (balanced mid-tier) and Luna (high-volume, budget) — each sharing a 1.05-million-token context window. Pricing here moves fast enough that any figure needs a date attached: on July 30, 2026, OpenAI cut Terra's price by 20% and Luna's by a striking 80%, the largest single price move since GPT-5 launched, in what multiple industry analysts are now openly calling a price war among frontier AI labs. As of that cut, Luna runs $0.20/M input and $1.20/M output tokens — roughly 4x cheaper than the previous-generation GPT-5.4 Mini despite sitting in the current flagship family — while Sol remains at $5/M input, $30/M output for the hardest reasoning and agentic workloads. Cached input reads bill at just 10% of standard rates, and the Batch API halves both input and output costs for asynchronous jobs completed within 24 hours. The practical implication for anyone budgeting an OpenAI-powered feature: tier choice, not just vendor choice, is now the single biggest lever on your bill, and prices that were accurate even a month or two ago may already be stale. For simple classification, summarization or routing tasks, Luna or GPT-5 Nano deliver adequate results at a small fraction of Sol's cost; reserve the expensive frontier tier specifically for genuinely difficult reasoning, coding or multi-step agent work where the capability gap actually matters.

Anthropic API

Trending

Build with Claude — Haiku, Sonnet and Opus at per-token rates

The Anthropic API gives developers programmatic access to the Claude model family — Haiku (fast, low-cost), Sonnet (balanced), and Opus (most capable) — for building applications ranging from customer support bots to autonomous coding agents, billed per million tokens with output priced roughly 5x higher than input across the lineup. As of mid-2026, current rates run Haiku 4.5 at $1/$5 per million input/output tokens, Sonnet 4.6 at $3/$15, and Opus 4.8 at $5/$25 — with all three supporting a full 1-million-token context window at standard rates and no long-context surcharge, a meaningful simplification for teams that previously had to budget for context-length premiums. A genuinely significant pricing shift happened with Opus: the current $5/$25 rate represents roughly a 3x cut from the older Opus 3 generation's $15/$75, making Anthropic's most capable model dramatically more accessible than it was a year or two earlier. Prompt caching cuts cached-input costs by 90% (Sonnet 4.6 cached reads drop to $0.30/M from $3.00/M), and Batch processing offers a flat 50% discount across every model for asynchronous, non-urgent workloads — stacking both optimizations is where most production teams find the biggest savings. There's no ongoing free API tier — new accounts receive a modest trial credit ($5 in some documented cases) to test integration before committing to paid usage, separate entirely from Claude.ai's consumer subscription plans, which don't include API access. For teams choosing between models, the practical guidance holds steady: route routine, latency-sensitive work (extraction, classification, short replies) to Haiku, reserve Opus for genuinely complex reasoning or high-stakes tasks, and lean on caching aggressively wherever system prompts or tool definitions repeat across requests.

LangChain

The free, open-source framework for LLM apps — LangSmith observability costs extra

LangChain is a completely free, open-source Python and JavaScript framework for building LLM-powered applications — chains, agents, retrieval-augmented generation (RAG), and memory management, all through modular, composable components that work across virtually any model provider (OpenAI, Anthropic, Hugging Face and others) via a unified interface. This is worth stating plainly because pricing confusion around LangChain is common: the core framework itself has never charged anything and never will, since it's genuinely open source with community contributions. What does cost money is LangSmith, Anthropic's — sorry, LangChain's — separate observability and debugging platform for tracing, monitoring and evaluating LLM application behavior in production: free for up to 5,000 traces/month, Plus at $39/month for higher volume and team features, and custom Enterprise pricing above that. LangGraph, a related framework for building more structured, stateful multi-agent workflows, is also open source and free, distinct from both the core LangChain library and LangSmith. The practical cost of running a LangChain-based application, then, is really the sum of your underlying LLM API costs (OpenAI, Anthropic, etc.) plus, optionally, LangSmith if you want production observability — LangChain itself contributes zero licensing cost either way. It remains one of the most widely adopted frameworks for prototyping and building LLM applications specifically because of that combination: genuinely free tooling, broad multi-provider support, and an extensive integration ecosystem, though developers still handle their own deployment, infrastructure and scaling rather than getting a managed, turnkey hosting layer.

Ollama

Featured

Run open LLMs locally with one command

Ollama makes local language models genuinely easy. A single command pulls a quantised model — Llama, Mistral, Gemma, Qwen, DeepSeek and hundreds more — and starts serving it with an OpenAI-compatible HTTP endpoint on your own machine. That compatibility is the reason it spread so quickly: existing code written against the OpenAI SDK usually works by changing the base URL, so developers can prototype against a local model at zero marginal cost, then swap to a hosted provider for production. Modelfiles let you bake a system prompt, parameters and adapters into a reusable named model. Because nothing leaves the device, Ollama is also the default answer for regulated work, offline environments and anyone experimenting with fine-tunes on consumer hardware.

Groq

Trending

The fastest LLM inference on custom LPU chips — now backed by a $20B NVIDIA deal

Groq built custom LPU (Language Processing Unit) hardware specifically optimized for LLM inference speed, and it shows in the numbers: Llama 3.3 70B runs at roughly 394 tokens/second on Groq's chips, with smaller models like Llama 3.1 8B hitting around 840 tokens/second — speeds that make it the go-to choice for latency-sensitive applications like real-time voice agents or interactive chat where every millisecond of response time is felt by the user. The commonly cited "10-20x cheaper than OpenAI" figure is roughly accurate but needs a caveat: that's comparing Groq's open-model pricing against OpenAI's proprietary models, not identical capability tiers. A major corporate development reshapes how to think about Groq's future: NVIDIA announced a $20 billion deal for Groq in the period following Christmas 2025, though as of mid-2026 the GroqCloud platform remains live and its public pricing page unchanged. Groq pricing starts at $0.05/M input tokens for Llama 3.1 8B Instant, scaling up to $0.90/M tokens for larger vision models — and in direct model-for-model comparisons against Together AI, Groq wins on price for 6 of 11 shared models, while Together wins on 3 and 2 tie, reflecting genuine competitive parity rather than one clearly cheaper provider. The honest limitation: Groq serves a fixed catalog of models (Llama, GPT-OSS, Qwen, Kimi K2, DeepSeek and others) optimized specifically for its LPU hardware — it doesn't host GPT-5, Claude or Gemini, so apps depending on those specific proprietary models need to stay with their original provider or route through an aggregator like OpenRouter. For latency-critical applications running a model Groq's catalog covers, its speed advantage is genuinely difficult for GPU-based competitors to match; for anything requiring proprietary frontier models or custom fine-tuned weights, Groq isn't the right tool.

OpenAI API

Trending

Build with GPT-5.6 — pricing that shifts with a genuine AI price war

OpenAI's API gives developers programmatic access to the GPT model family for building chatbots, agents, content generation and any application needing language understanding, billed per token rather than a flat subscription. The current flagship lineup, GPT-5.6, reached general availability on July 9, 2026 across three tiers — Sol (frontier reasoning and complex agent tasks), Terra (balanced mid-tier) and Luna (high-volume, budget) — each sharing a 1.05-million-token context window. Pricing here moves fast enough that any figure needs a date attached: on July 30, 2026, OpenAI cut Terra's price by 20% and Luna's by a striking 80%, the largest single price move since GPT-5 launched, in what multiple industry analysts are now openly calling a price war among frontier AI labs. As of that cut, Luna runs $0.20/M input and $1.20/M output tokens — roughly 4x cheaper than the previous-generation GPT-5.4 Mini despite sitting in the current flagship family — while Sol remains at $5/M input, $30/M output for the hardest reasoning and agentic workloads. Cached input reads bill at just 10% of standard rates, and the Batch API halves both input and output costs for asynchronous jobs completed within 24 hours. The practical implication for anyone budgeting an OpenAI-powered feature: tier choice, not just vendor choice, is now the single biggest lever on your bill, and prices that were accurate even a month or two ago may already be stale. For simple classification, summarization or routing tasks, Luna or GPT-5 Nano deliver adequate results at a small fraction of Sol's cost; reserve the expensive frontier tier specifically for genuinely difficult reasoning, coding or multi-step agent work where the capability gap actually matters.

Anthropic API

Trending

Build with Claude — Haiku, Sonnet and Opus at per-token rates

The Anthropic API gives developers programmatic access to the Claude model family — Haiku (fast, low-cost), Sonnet (balanced), and Opus (most capable) — for building applications ranging from customer support bots to autonomous coding agents, billed per million tokens with output priced roughly 5x higher than input across the lineup. As of mid-2026, current rates run Haiku 4.5 at $1/$5 per million input/output tokens, Sonnet 4.6 at $3/$15, and Opus 4.8 at $5/$25 — with all three supporting a full 1-million-token context window at standard rates and no long-context surcharge, a meaningful simplification for teams that previously had to budget for context-length premiums. A genuinely significant pricing shift happened with Opus: the current $5/$25 rate represents roughly a 3x cut from the older Opus 3 generation's $15/$75, making Anthropic's most capable model dramatically more accessible than it was a year or two earlier. Prompt caching cuts cached-input costs by 90% (Sonnet 4.6 cached reads drop to $0.30/M from $3.00/M), and Batch processing offers a flat 50% discount across every model for asynchronous, non-urgent workloads — stacking both optimizations is where most production teams find the biggest savings. There's no ongoing free API tier — new accounts receive a modest trial credit ($5 in some documented cases) to test integration before committing to paid usage, separate entirely from Claude.ai's consumer subscription plans, which don't include API access. For teams choosing between models, the practical guidance holds steady: route routine, latency-sensitive work (extraction, classification, short replies) to Haiku, reserve Opus for genuinely complex reasoning or high-stakes tasks, and lean on caching aggressively wherever system prompts or tool definitions repeat across requests.

Hugging Face

Trending

The largest open-source AI hub — 800,000+ models, free to browse

Hugging Face is the closest thing the AI industry has to GitHub: a hub hosting over 800,000 open-source models, 100,000+ datasets, and Spaces (hosted demo apps), all free to browse, download and use, with a thriving community driving much of its content. Beyond the hub itself, its Inference offerings split into three distinct products, a naming overlap that trips up newcomers: the Serverless Inference API (free tier with rate limits, best for prototyping), Inference Endpoints (dedicated GPU instances you spin up per model, starting around $0.50-0.60/hour with scale-to-zero), and Inference Providers (a unified OpenAI-compatible gateway routing to 15+ third-party providers including Groq, Together AI, Fireworks and Replicate). Inference Providers is the more recent addition, maturing through 2025-2026 as Hugging Face's answer to "which inference partner should I use" — rather than picking one provider directly, you call Hugging Face's single endpoint and it routes to whichever underlying provider hosts the model, with pricing passed through at each provider's own published rate. Free users get a small monthly credit allocation ($0.10) toward this routed inference, while PRO subscribers ($9/month) get $2.00 in monthly credits plus faster rate limits and private Spaces — a meaningful upgrade for anyone doing more than light experimentation. For teams choosing between Hugging Face and Replicate specifically: Hugging Face wins decisively on sheer model breadth and community size, while Replicate offers a somewhat simpler, more image-generation-optimized API experience. For teams choosing between Hugging Face and LangChain: they're not really competitors — Hugging Face is model hosting and inference, LangChain is an application-building framework — and many production stacks genuinely use both together.

Groq

Trending

The fastest LLM inference on custom LPU chips — now backed by a $20B NVIDIA deal

Groq built custom LPU (Language Processing Unit) hardware specifically optimized for LLM inference speed, and it shows in the numbers: Llama 3.3 70B runs at roughly 394 tokens/second on Groq's chips, with smaller models like Llama 3.1 8B hitting around 840 tokens/second — speeds that make it the go-to choice for latency-sensitive applications like real-time voice agents or interactive chat where every millisecond of response time is felt by the user. The commonly cited "10-20x cheaper than OpenAI" figure is roughly accurate but needs a caveat: that's comparing Groq's open-model pricing against OpenAI's proprietary models, not identical capability tiers. A major corporate development reshapes how to think about Groq's future: NVIDIA announced a $20 billion deal for Groq in the period following Christmas 2025, though as of mid-2026 the GroqCloud platform remains live and its public pricing page unchanged. Groq pricing starts at $0.05/M input tokens for Llama 3.1 8B Instant, scaling up to $0.90/M tokens for larger vision models — and in direct model-for-model comparisons against Together AI, Groq wins on price for 6 of 11 shared models, while Together wins on 3 and 2 tie, reflecting genuine competitive parity rather than one clearly cheaper provider. The honest limitation: Groq serves a fixed catalog of models (Llama, GPT-OSS, Qwen, Kimi K2, DeepSeek and others) optimized specifically for its LPU hardware — it doesn't host GPT-5, Claude or Gemini, so apps depending on those specific proprietary models need to stay with their original provider or route through an aggregator like OpenRouter. For latency-critical applications running a model Groq's catalog covers, its speed advantage is genuinely difficult for GPU-based competitors to match; for anything requiring proprietary frontier models or custom fine-tuned weights, Groq isn't the right tool.