The question “which AI tool should we use?” has become meaningless. What matters now is “which model for which task, and under what constraints?”
The landscape has fragmented. There’s no single dominant model anymore.
Instead, you’re choosing between different families with different strengths.
OpenAI’s GPT-5.4 line is strong for high-end coding and agentic workflows. Anthropic’s Claude 4.6 family stands out for long-context and coding strength. Google’s Gemini 3 line is strong on speed and massive context windows.
Then there’s DeepSeek, with aggressive pricing but some concerning security issues. There are also open-weight alternatives like Qwen, Llama, and Mistral that run locally.
Each has genuine trade-offs. And each makes sense in different contexts.
In this article, we’ll walk examine each major family in depth to help you choose a model stack that fits your needs.
Let’s dive in!
Table of Contents
How AI models actually differ today
Two years ago, “AI for coding” mostly meant choosing between a small number of general-purpose assistants.
That is no longer true.
Today, the landscape is split across higher-capability coding models, lower-cost fast models, reasoning-heavy models, long-context models, and open-weight models you can run yourself.
The latest lineups from OpenAI, Anthropic, Google, Qwen, Meta, and Mistral all reflect that shift.
That happened for a few reasons:
Models became more specialized.
Benchmarks started measuring more realistic engineering work.
Open-weight models became good enough for serious use cases.
IDEs turned model choice into an ongoing routing decision, not a one-time choice.
Models are now more clearly separated by role.
Anthropic splits Claude Opus 4.6, Sonnet 4.6, and Haiku 4.5 by capability, speed, and cost.
OpenAI does something similar with GPT-5.4 and GPT-5.4 mini. Google splits its Gemini line between Gemini 3.1 Pro Preview and Gemini 3 Flash Preview.
Open-weight vendors have done the same.
Qwen now has Qwen3-Coder models built specifically for coding. Meta has moved to the Llama 4 family. Mistral separates Codestral from its larger general-purpose models.
Benchmark priorities changed too. Basic code-completion benchmarks matter less than they used to.
Providers now emphasize more realistic engineering and agentic benchmarks, especially SWE-bench-style evaluations, terminal-task benchmarks, and longer-horizon coding tests.
The most important split is still between fast general-purpose models and heavier reasoning models.
Fast models handle everyday coding, code review, refactoring, documentation, and routine debugging with lower latency and lower cost.
Heavier models make more sense when the problem is ambiguous, multi-step, or expensive to get wrong.
In practice, the goal is not to pick one best model but to match the right model to the right task.
AI model comparison table: cost and top use cases
Here’s an overview of the input and output costs and the top use cases for each of the major models on the market:
Model
Input cost
Output cost
Best for
Claude Sonnet 4.6
$3/M
$15/M
Serious development work, code quality, day-to-day coding
Claude Opus 4.6
$5/M
$25/M
Complex refactoring, full-codebase reasoning, long-running agentic work
Next, we’ll take a look at each model in more detail.
The OpenAI family: GPT-5.4 and GPT-5.4 mini
OpenAI’s current model lineup covers both ends of the spectrum for development work.
GPT-5.4 is the higher-capability option for complex coding and agentic tasks, while GPT-5.4 mini is the more practical choice for faster, lower-cost day-to-day use.
GPT-5.4 (the top-end GPT model)
GPT-5.4 is OpenAI’s current top GPT-family model for development work.
Input costs $2.50 per million tokens, cached input costs $0.25 per million, and output costs $15.00 per million.
It supports a 1,050,000-token context window and up to 128,000 output tokens.
The much larger context window makes GPT-5.4 a better fit for large codebases, multi-file changes, and repo-wide tasks where the model needs to keep track of more moving parts across services.
Use GPT-5.4 for: complex coding tasks, multi-file changes, repo-wide reasoning, agentic development workflows, and work where accuracy matters more than cost.
GPT-5.4 mini (the practical lower-cost option)
GPT-5.4 mini is the practical lower-cost GPT-family option. OpenAI describes it as its strongest mini model yet for coding, computer use, and subagents.
Input costs $0.75 per million tokens, cached input costs $0.075 per million, and output costs $4.50 per million.
It supports a 400,000-token context window and up to 128,000 output tokens.
This is the model to use when you want strong coding performance without paying top-tier model rates on every task.
GPT-5.4 mini brings the strengths of GPT-5.4 to a faster, more efficient model designed for high-volume workloads.
Use GPT-5.4 mini for: day-to-day coding tasks, implementation work, debugging, tool-using workflows, and teams that want a strong cost/performance balance.
The Anthropic / Claude family: context and coding strength
Claude remains one of the strongest model families for software development. Anthropic’s current lineup is built around Claude Opus 4.6, Claude Sonnet 4.6, and Claude Haiku 4.5.
Claude Sonnet 4.6 (the daily driver)
Claude Sonnet 4.6 is Anthropic’s best combination of speed and intelligence.
It costs $3.00 per million input tokens and $15.00 per million output tokens. It supports a 1M token context window and up to 64K output tokens.
Anthropic reports 72.7% on SWE-bench for Claude Sonnet 4. Anthropic also reports a high-compute SWE-bench score of 80.2% for Sonnet 4.
The 1M token context window gives Sonnet 4.6 much more room for large codebases, multi-file work, and tasks where the model needs to keep track of architecture, implementation details, and supporting documentation at the same time.
Use Claude Sonnet 4.6 for: code generation, refactoring, code review, architecture questions, debugging, feature implementation, and day-to-day development work.
Claude Opus 4.6 (late-stage reasoning and extended context)
Claude Opus 4.6 is Anthropic’s most intelligent model for building agents and coding.
It costs $5.00 per million input tokens and $25.00 per million output tokens. It supports a 1M token context window and up to 128K output tokens.
Anthropic reports 72.5% on SWE-bench and 43.2% on Terminal-bench for Claude Opus 4. Anthropic also reports a high-compute SWE-bench score of 79.4% for Opus 4.
The 1M token context window makes Opus 4.6 a strong fit for very large codebases, major multi-file changes, and workflows where context is critical.
Use Claude Opus 4.6 for:major refactoring work, designing new systems with full codebase awareness, complex multi-file changes, and long-running agentic workflows.
Claude Haiku 4.5 (budget tier for rapid work)
Claude Haiku 4.5 is Anthropic’s fastest current model with near-frontier intelligence.
It costs $1.00 per million input tokens and $5.00 per million output tokens. It supports a 200K token context window and up to 64K output tokens.
Haiku 4.5 is the lower-cost Claude option for high-volume and latency-sensitive work. Anthropic positions it as the fastest model in the lineup.
Use Haiku 4.5 for: IDE code completions, quick refactoring suggestions, test generation, documentation, and other high-volume, repeatable tasks.
The Google / Gemini family: speed and massive context
Google’s Gemini models stand out for long context windows, multimodal inputs, and relatively aggressive pricing on the Flash line.
In the current lineup, Gemini 3.1 Pro is the higher-capability model for complex work, while Gemini 3 Flash is the faster, cheaper option for high-frequency development workflows.
Gemini 3.1 Pro (balanced capability with massive context)
Gemini 3.1 Pro is Google’s higher-capability Gemini model for complex tasks that require broader world knowledge and stronger reasoning.
It costs $2.00 per million input tokens and $12.00 per million output tokens for prompts under 200K tokens, and $4.00 per million input and $18.00 per million output for prompts over 200K tokens.
It supports a 1,048,576-token input window and 65,536 output tokens.
Google describes Gemini 3.1 Pro as optimized for software engineering behavior, agentic workflows, improved token efficiency, and more reliable multi-step execution.
Gemini 3.1 Pro’s 1M-token context window makes it a strong fit for large codebases and repo-wide work where the model needs to track architecture, implementation details, and documentation at the same time.
Use Gemini 3.1 Pro for: complex coding tasks, multi-file changes, repo-wide reasoning, agentic development workflows, and when you need both strong capability and very large context.
Gemini 3 Flash (the speed-first option)
Gemini 3 Flash is Google’s fast, lower-cost model for iterative development.
It costs $0.50 per million input tokens and $3.00 per million output tokens. It supports a 1,048,576-token input window and 65,536 output tokens.
On SWE-bench Verified, Google reports that Gemini 3 Flash scores 78%.
Google positions it as offering Pro-grade coding performance with lower latency, making it a strong fit for iterative development and responsive coding workflows.
The 1M-token context window is large enough for substantial codebases, long documentation sets, and multi-file workflows in a single prompt.
Gemini 3 Flash also supports thinking, code execution, function calling, file search, and computer use.
Use Gemini 3 Flash for: cost-sensitive work, rapid iteration, batch processing, and IDE-style assistance.
DeepSeek’s current public API lineup centers on DeepSeek-V3.2 for general and agentic work and DeepSeek-R1 for reasoning tasks.
In DeepSeek’s API docs, deepseek-chat and deepseek-reasoner both map to DeepSeek-V3.2, with deepseek-chat as the non-thinking mode and deepseek-reasoner as the thinking mode.
Current pricing for deepseek-reasoner is $0.55 per million input tokens (cache miss), $0.14 per million cached input, and $2.19 per million output tokens.
In DeepSeek’s official benchmark table, R1 is listed at 96.3 percentile on Codeforces, 65.9 on LiveCodeBench Pass@1-COT, and 49.2% on SWE-bench Verified (Resolved).
Use DeepSeek-R1 for: hard reasoning, multi-step debugging, competitive programming, and other complex problems.
DeepSeek’s documented security issues
DeepSeek’s privacy policy states that it collects account data, prompts, uploaded files, chat history, device and network data, and other usage information.
In February 2025, South Korea’s Personal Information Protection Commission said DeepSeek had temporarily suspended its service in Korea to improve compliance with the country’s privacy law.
In July 2025, the Czech Republic’s NÚKIB issued a formal warning covering DeepSeek products, websites, web services, and the API.
So, if you handle sensitive customer data or work in a highly-regulated industry, these security risks outweigh the benefits of using DeepSeek’s models.
Qwen (Alibaba): open-weight coding capability
Qwen’s coding lineup now centers on Qwen3-Coder.
The main local-development option is Qwen3-Coder-Next, an open-weight coding model built for agentic workflows and repository-scale coding tasks.
Qwen3-Coder-Next (the local coding model)
Qwen3-Coder-Next is an 80B-parameter model with 3B active parameters. It supports a 256K token context window natively, with extension up to 1M tokens using YaRN.
Qwen reports that Qwen3-Coder-Next achieves over 70% on SWE-Bench Verified using the SWE-Agent scaffold. Qwen also says it remains competitive across multilingual and long-horizon agentic coding tasks.
Qwen3-Coder-Next supports 358 coding languages. The model is released under the Apache 2.0 license.
Use Qwen3-Coder-Next for: privacy-critical work, on-premise deployments, cost-sensitive batch processing, and local coding agents.
Llama (Meta): self-hosting with data control
The main Llama models now are Llama 4 Maverick and Llama 4 Scout.
Llama models are downloadable for self-hosted use, but the current Llama 4 models are released under the Llama 4 Community License Agreement, not an OSI-approved open-source license.
Llama 4 Maverick is a mixture-of-experts model with 17 billion active parameters and 128 experts.
Meta describes it as its most powerful open-weight multimodal model in the current Llama line and says it beats GPT-4o and Gemini 2.0 Flash across a broad range of reported benchmarks, while achieving comparable results to DeepSeek v3 on reasoning and coding.
Maverick is designed for complex reasoning and multimodal tasks.
Meta’s deployment docs describe it as a high-capability model with 17B active parameters from 400B total.
Use Llama 4 Maverick for: self-hosted deployments that need stronger reasoning, coding-adjacent work, and more capable local or private inference.
Llama 4 Scout (the deployment-friendly option)
Llama 4 Scout is the more infrastructure-efficient model in the Llama 4 family.
Meta describes it as a natively multimodal model with single H100 GPU efficiency and a 10M token context window.
The large context window makes Scout a strong fit for long documents, large codebases, and workflows where keeping more material in one prompt matters more than reaching the highest reasoning ceiling.
Use Llama 4 Scout for: privacy-first deployments, long-context workloads, and if you want self-hosted inference with lower hardware demands than frontier-scale models.
Mistral: EU-based and coding-specialized
Mistral’s current lineup includes Codestral 25.08 for code generation and Mistral Large 3 for general-purpose work.
Mistral is a European company, and prioritizes providers within the European Union.
And when personal data is processed outside the EU, it uses safeguards compliant with Article 46 of the GDPR.
Codestral 25.08 (the coding-specialized model)
Codestral 25.08 is Mistral’s coding model for code completion, fill-in-the-middle, and code generation.
It supports a 128K context window and costs $0.30 per million input tokens and $0.90 per million output tokens.
Mistral’s later Codestral 25.01 release says the model is about 2x faster than the original and is the clear leader for coding in its weight class, with state-of-the-art fill-in-the-middle performance.
Use Codestral 25.08 for: code completion, code generation, developer copilots, fill-in-the-middle workflows, and other coding-specific tasks where low latency matters.
Mistral Large 3 (the general-purpose model)
Mistral Large 3 is Mistral’s current state-of-the-art open-weight general-purpose multimodal model.
It supports a 256K context window and costs $0.50 per million input tokens and $1.50 per million output tokens.
The Mistral Large 3 model is now the current general-purpose option in Mistral’s lineup.
Use Mistral Large 3 for: general-purpose work, multilingual reasoning, long-context tasks, and when you want a Mistral flagship model instead of a coding-only specialist.
How to pick the right AI model for software development
Model selection now happens inside IDEs as much as it happens in procurement.
Cursor, Windsurf, GitHub Copilot, JetBrains AI Assistant, and other IDEs all act as routing layers between developers and model providers.
That means model choice is increasingly continuous and task-specific, not a one-time decision. A practical model stack usually looks something like this:
One fast, lower-cost model for routine work
One stronger model for serious engineering tasks
One local or privacy-first option for sensitive workloads
The open-versus-closed trade-off still matters, but it is easier to frame now.
Closed models still lead on convenience and usually remain strongest on the hardest coding and reasoning tasks.
Open-weight models give you more control over where data goes, how systems are deployed, and how costs behave at scale.
The trade-off is operational burden. If you self-host, you take on infrastructure, maintenance, and observability yourself.
The best way to choose is still the simplest one: test the models on your own codebase, inside your own tooling, and measure quality, latency, and cost together.
Conclusion
Choosing an AI model today isn’t about picking the “best” one. It’s about picking the right one for your product, your team, and your constraints.
Each model we’ve covered has a clear role. Some are great for deep reasoning. Others shine in speed, cost, or flexibility. The real value comes from knowing when to use each of them.
In practice, most dev teams don’t rely on a single model.
The key is to stay pragmatic. Test in your own environment, measure what actually matters to your product, and avoid overengineering your setup too early.
Skilled in React Native, iOS and backend, Toni has a demonstrated knowledge of the information technology and services industry, with plenty of hands-on experience to back it up. He’s also an experienced Cloud engineer in Amazon Web Services (AWS), passionate about leveraging cloud technologies to improve the agility and efficiency of businesses.
One of Toni’s most special traits is his talent for online shopping. In fact, our delivery guy is convinced that ‘Toni Vujević’ is a pseudonym for all DECODErs.
