Best AI Models for Developers - February 2026

The landscape of AI models for developers is evolving at breakneck speed. After testing and comparing the latest versions, we present the definitive guide for choosing the best model based on your specific use case.

What changed in February 2026:

GPT-5.3-Codex arrives and dominates Terminal-Bench with 77.3%
Claude Opus 4.5 breaks the 80% barrier on SWE-bench Verified (81.6%)
Claude Opus 4.6 introduces Agent Teams for parallel work
GPT-5.3-Codex classified "High capability" in cybersecurity (restricted access)
Gemini 3 Flash emerges as performance/price champion

Summary Table

Model	Terminal-Bench 2.0	SWE-bench	Price/M tokens²	Status	Best for
GPT-5.3-Codex	77.3%	Pro: 64.7%	TBA	Production	Autonomous CLI, multi-day agent
GPT-5.2-Codex	64.0%	Pro: 56.4%	TBA	Replaced	Massive refactoring, Windows
Claude Opus 4.6	65.4%	-	$5/$25	Production	Agent teams, 1M context
Claude Opus 4.5	59.8%	Verified: 81.6%	$5/$25	Production	Python GitHub issues
Claude Sonnet 4.5	50.0%	Verified: 77.2%	$3/$15	Production	Daily use, 30h+
Gemini 3 Flash	-	78%	$0.075/$0.30	Production	Ultra-fast prototyping
GPT-5	-	Verified: 74.9%	$1.25/$10	Production	General use
DeepSeek R1	-	71-72%	$1.35/$4.20	Production	Open-source

²Price = input/output per million tokens USD

Champions by Category

1. 🖥️ Terminal & CLI Automation

Champion: GPT-5.3-Codex (77.3%)

The new GPT-5.3-Codex crushes Terminal-Bench 2.0 with a 13-point lead over its nearest competitor. It's the best model ever created for terminal automation.

✅ Excels at:

Complex multi-stage DevOps pipelines
Bash/zsh scripts with advanced error handling
Real-time debugging of failing commands
Infrastructure automation (Kubernetes, Terraform, etc.)
Multi-day agent sessions without context loss

Real example: Capable of debugging a CI/CD pipeline that fails at the 15th step, identifying the permission issue, proposing 3 solutions, and implementing the chosen one — all in a single session.

Alternative: Claude Opus 4.6 (65.4%)

Major innovation: Agent Teams. Opus 4.6 can now orchestrate multiple agents in parallel.

✅ Excels at:

Multi-agent orchestration (code review while tests run)
Long-duration maintenance scripts (migrations, cleanups)
Massive context (1M tokens = entire codebase)
Optimized read-heavy workflows

When to choose Opus 4.6 over GPT-5.3-Codex?

You need to read a huge codebase before acting
You want to parallelize independent tasks
You prefer the Anthropic ecosystem (more transparent)

Budget-friendly: Gemini 3 Flash

Unbeatable price: $0.075/$0.30 per million tokens (up to 80x cheaper than Opus 4.6!).

✅ Excels at:

Simple scripts and rapid prototyping
Basic CI/CD automation
Standard command generation
Quick idea testing

⚠️ Limitation: Less reliable on complex or ambiguous tasks.

2. 🐛 GitHub Issues & Bug Fixing

Champion: Claude Opus 4.5 (81.6% SWE-bench Verified)

Historic performance: First model to exceed 80% on SWE-bench Verified, the most difficult benchmark based on real GitHub issues (500 real Python issues from Django, Flask, Scikit-learn, etc.).

✅ Excels at:

Complex Python issues requiring deep understanding
Production-ready patches (not throwaway code)
Established open-source projects (Django, Flask, Requests, etc.)
Bugs requiring extensive context reading

Impressive statistics:

81.6%: Resolves more than 4 out of 5 issues in complete autonomy
80.9% according to some sources (evaluation variation)
Best Python model in the entire industry

Real use case:

Issue: "Django ORM generates an incorrect SQL query 
when using .select_related() with 
prefetch_related() on a ManyToMany relation 
after a database migration."

Opus 4.5:
1. Reads select_related and prefetch_related code
2. Identifies bug in query cache handling
3. Proposes a 12-line patch
4. Adds 2 regression tests
✅ Accepted in production

Multi-language: GPT-5.3-Codex (64.7% SWE-bench Pro)

SWE-bench Pro is harder than Verified:

Multi-language (Python, JavaScript, Java, Go, Rust, C++)
Contamination-resistant (issues after training cutoff)
Polyglot projects (frontend + backend + infra)

GPT-5.3-Codex dominates this category with an 8-point lead over second place (GPT-5.2-Codex at 56.4%).

✅ Excels at:

Projects with multiple programming languages
JavaScript/TypeScript issues (React, Node.js, etc.)
Infrastructure bugs (Docker, Kubernetes configs)
Less mainstream projects (Go, Rust, etc.)

When to choose GPT-5.3 over Opus 4.5?

Your project isn't Python-only
You're working on post-2024 code (avoid contamination)
You need terminal/CLI expertise in addition to the fix

Budget-conscious: Gemini 3 Flash (78%)

Impressive: 78% on SWE-bench for only $0.075/$0.30 per million tokens.

✅ Excels at:

Simple to medium well-documented bugs
Projects with existing tests (model can iterate)
Prototyping fixes before production

3. 🔄 Refactoring & Massive Migrations

Champion: GPT-5.2-Codex / GPT-5.3-Codex

OpenAI's Codex models have a unique feature: context compaction. They can maintain a coherent session on 100k+ line codebases without losing the thread.

✅ Excels at:

Framework migrations (React 16 → 18, Angular → React, etc.)
Massive architectural refactoring (monolith → microservices)
Intelligent renaming across entire codebase
Long sessions (several days) with maintained context

Real case:

Migration of a React 16 app (150k lines) to React 18:
- Conversion of class components → functional + hooks
- Replacement of lifecycle methods
- Migration from PropTypes → TypeScript
- Tests automatically updated
⏱️ 6 days with GPT-5.2-Codex vs 3+ weeks manually

Why not Claude Opus 4.6 with its 1M context?

Opus 4.6 can read 1M tokens, but GPT-5.x-Codex is better at planning and executing sequential changes over several days. Context compaction maintains design decisions even after thousands of edited lines.

Exception: If your refactoring requires reading the entire codebase before touching anything, Opus 4.6 with Agent Teams might be better (one agent reads, another plans, a third executes).

4. 🪟 Windows Development

Champion: GPT-5.2-Codex / GPT-5.3-Codex

OpenAI made native Windows improvements in Codex versions 5.2 and 5.3.

✅ Excels at:

Advanced PowerShell scripts
.NET development (C#, F#, VB.NET)
WSL integration (Windows Subsystem for Linux)
Windows automation (Registry, Task Scheduler, etc.)
Git Bash and MINGW64 compatibility

Windows-specific performance:

Understands differences between PowerShell 5.1 and PowerShell 7
Correctly handles Windows paths (C:\Users\...)
Knows the specifics of CMD vs PowerShell vs Git Bash
Proposes cross-platform solutions when relevant

Use case:

# GPT-5.3-Codex generates idiomatic PowerShell
Get-ChildItem -Path "C:\Projects" -Recurse -Filter "*.cs" |
  Where-Object { $_.LastWriteTime -gt (Get-Date).AddDays(-7) } |
  ForEach-Object {
    $content = Get-Content $_.FullName
    if ($content -match "TODO|FIXME") {
      [PSCustomObject]@{
        File = $_.FullName
        Line = ($content | Select-String "TODO|FIXME").LineNumber
      }
    }
  } | Export-Csv -Path "todos.csv" -NoTypeInformation

Alternative: Claude Sonnet 4.5

Excellent for cross-platform development in general, but less Windows-specialized.

✅ Better than GPT-5.x-Codex for:

Node.js/Python projects running on Windows and Linux
When you want portable solutions by default
Reduced budget ($3/$15 vs TBA for Codex)

5. 🔐 Cybersecurity & Vulnerability Research

Champion: GPT-5.3-Codex

⚠️ IMPORTANT: GPT-5.3-Codex is the first model classified "High capability" in cyber by OpenAI. Access is restricted to verified security researchers.

Why this restriction?

GPT-5.2-Codex (the predecessor) demonstrated concerning capabilities:

🚨 CVEs discovered by GPT-5.2-Codex:

CVE-2025-55182: React vulnerability (CVSS 10.0 — critical maximum)
CVE-2025-55183, 55184, 67779: Other 0-day vulnerabilities

Workflow used:

Automatic iterative fuzzing
Parallel source code analysis
Local environment for testing exploits
Detailed report with POC

GPT-5.3-Codex goes even further:

✅ Capabilities (under supervision):

Automatic 0-day vulnerability discovery
Intelligent multi-language fuzzing
Binary reverse engineering
Malware analysis (without execution)
Automated pentesting

Who can access it?

Security researchers employed by verified organizations
Bug bounty hunters with proven track record
Red Team units from companies with OpenAI agreement
Vetting process: 2-4 weeks

⚠️ For non-cyber-specialized developers: Use Claude Opus 4.5 for general security review (SQL injection, XSS, CSRF, etc.). It's excellent without requiring restricted access.

6. 🎨 Frontend Development & UI/UX

Champion: GPT-5 (70% preferred in user studies)

Surprise: generalist GPT-5 (not Codex) is the favorite for frontend development.

Why GPT-5 rather than GPT-5.x-Codex?

GPT-5 excels at creative and aesthetic tasks:

✅ Excels at:

Aesthetic interface design
Spacing, typography, harmonious colors
Intuitive responsive design
Apps/games generation from scratch
React components "beautiful by default"

User study (January 2026):

70% of frontend developers prefer GPT-5 for UI/UX
58% prefer Claude Opus for complex frontend business logic
45% use both: GPT-5 for design, Claude for architecture

Use case:

"Create a landing page for a cybersecurity SaaS 
startup. Modern, minimalist style, with 
subtle animations. Dark mode by default."

GPT-5 generates:
✅ Coherent and professional design
✅ Smooth Framer Motion animations
✅ Harmonious color palette
✅ Perfect mobile responsive
✅ Accessibility (ARIA, contrast)

Alternative: Claude Opus 4.5

Better than GPT-5 for:

Complex frontend architecture (state management, routing)
Reusable React components with strict TypeScript
Performance optimization (memoization, lazy loading)

Winning combination:

GPT-5: Initial design and visual prototyping
Claude Opus 4.5: Refactoring into clean components + architecture

7. 📚 Massive Codebase Review

Champion: Claude Opus 4.6

The Agent Teams innovation is a game-changer for massive code reviews.

✅ Excels at:

Parallel multi-agent review (one agent per module)
1M token context = entire codebase loaded
Optimized read-heavy workflows
Detailed report generation

How it works:

Example: Review of an 800k line monorepo

Agent Teams:
- Agent 1: Backend review (API, database)
- Agent 2: Frontend review (React components, state)
- Agent 3: Tests review (coverage, quality)
- Agent 4: Infra review (Docker, CI/CD)

Each agent:
1. Reads all relevant context (up to 250k tokens each)
2. Identifies issues in their zone
3. Consolidated summary in 20 minutes

vs sequential GPT-5.3-Codex: 2-3 hours

⚠️ Limitation: Agent Teams is a beta feature. Not all tools (VS Code, Cursor, etc.) support it yet.

Alternative: GPT-5.3-Codex remains excellent for deeper sequential reviews where analysis order matters.

Price/Performance Analysis

"Premium" Category ($5+ per M tokens output)

Claude Opus 4.6: $5/$25

Justified if: 1M context needed OR Agent Teams critical
ROI: Reduces review time by 70% on large codebases

Claude Opus 4.5: $5/$25

Justified if: Python GitHub issues OR complex architecture
ROI: 81.6% resolution rate = saves days of debugging

"Mainstream" Category ($10-$20 per M tokens output)

GPT-5: $1.25/$10

Best generalist quality/price ratio
Versatile for 90% of daily tasks

Claude Sonnet 4.5: $3/$15

Alternative to GPT-5 if you prefer Anthropic
Slightly more expensive but 200k token context (vs 128k for GPT-5)

"Budget" Category (< $5 per M tokens output)

Gemini 3 Flash: $0.075/$0.30

80x cheaper than Opus 4.6
500x cheaper than Claude Sonnet 4.5
Surprising performance (78% SWE-bench)
Perfect use case: Prototyping, simple scripts, basic CI/CD

DeepSeek R1: $1.35/$4.20

Open-source (can be self-hosted)
71-72% SWE-bench (very competitive)
Unique advantage: Total confidentiality if hosted locally

Recommendations by Profile

Solo Developer / Freelance

Recommended stack:

Gemini 3 Flash: Daily prototyping (economical)
Claude Opus 4.5: Complex GitHub issues (pay if critical bug)
GPT-5: UI/UX and general development

Estimated cost: $20-50 / month for 30h of assisted coding

Development Team (5-20 people)

Recommended stack:

GPT-5.3-Codex: DevOps pipelines and automation (team license)
Claude Opus 4.5: Reviews and Python issues
Claude Sonnet 4.5: Daily use (price/perf balance)
Gemini 3 Flash: CI/CD and automation scripts

Estimated cost: $500-2000 / month

Organization (50+ developers)

Recommended stack:

Claude Opus 4.6: Agent Teams for massive reviews
GPT-5.3-Codex: Critical refactorings and migrations
Claude Sonnet 4.5: Daily use (enterprise license)
DeepSeek R1 (self-hosted): Confidential internal code

Estimated cost: $10k-50k / month (but 10x-100x ROI)

Cybersecurity Researcher

Recommended stack:

GPT-5.3-Codex: Vulnerability research (restricted access required)
Claude Opus 4.5: Security code review
DeepSeek R1: Offline malware analysis

Note: GPT-5.3-Codex access request: 2-4 weeks of vetting.

Detailed Benchmarks

SWE-bench Verified (500 real Python issues)

Claude Opus 4.5: 81.6% (80.9% according to some sources)
GPT-5.2-Codex: 80.0%
Gemini 3 Flash: 78%
Claude Sonnet 4.5: 77.2%
GPT-5: 74.9%
DeepSeek R1: 71-72%

Why Verified is important:

Real issues from popular open-source projects
Django, Flask, Scikit-learn, Requests, SymPy, etc.
No benchmark "gaming" (evaluated by maintainers)

SWE-bench Pro (Multi-language, contamination-resistant)

GPT-5.3-Codex: 64.7% ⭐
GPT-5.2-Codex: 56.4%
GPT-5.2: 55.6%
GPT-5.1: 50.8%

Why Pro is harder:

Multi-language (not just Python)
Recent projects (post-training cutoff)
Ambiguous issues (short description, requires exploration)

GPT-5.3-Codex performance: +8 points over GPT-5.2-Codex (huge leap).

Terminal-Bench 2.0 (Real CLI commands)

GPT-5.3-Codex: 77.3% ⭐
Claude Opus 4.6: 65.4%
GPT-5.2-Codex: 64.0%
GPT-5.2: 62.2%
Claude Opus 4.5: 59.8%
Claude Sonnet 4.5: 50.0%

What Terminal-Bench measures:

Generation of bash/zsh/PowerShell commands
Debugging of failing commands
Multi-stage pipelines (with error handling)
Realistic DevOps automation

Gap GPT-5.3 vs Claude Opus 4.6: +12 points (massive domination).

OSWorld (Computer use agent)

Claude Opus 4.6: 72.7% ⭐
Claude Opus 4.5: 66.3%
GPT-5.3-Codex: 64.7%

What OSWorld measures:

Complete OS usage (clicks, navigation, files)
Multi-application tasks (browser + terminal + editor)
Visual understanding (screenshots)

Surprise: Claude Opus 4.6 dominates here (better than GPT-5.3-Codex). Probable reason: Agent Teams allows parallelization + wider context windows.

Predictions for March-April 2026

GPT-5.4-Codex (strong rumor)

Expected Terminal-Bench: 82-85%
Expected SWE-bench Pro: 70%+
Probable innovation: Multi-modal (screenshots + code)

Claude Opus 5.0

Expected SWE-bench: 85%+ (aiming for 90%)
Probable innovation: Agent Teams becomes stable (not beta)
Context window: 2M tokens (double 4.6)

Gemini 3 Pro

Middle ground between Flash and Ultra
Expected SWE-bench: 82-84%
Expected price: $1/$4 (between Flash and premium models)

The real game-changer: Computer Use

All models will integrate computer use (complete OS control). This fundamentally changes development:

AI launches VS Code, opens right files, edits, tests, debugs
AI navigates browser to search documentation
AI deploys to production via GUI (not just CLI)

Expected impact: Current benchmarks (SWE-bench, Terminal-Bench) will become obsolete. OSWorld will become the standard.

Conclusion: How to Choose?

Question 1: What's your main use case?

Terminal/CLI/DevOps → GPT-5.3-Codex
Python GitHub issues → Claude Opus 4.5
Massive refactoring → GPT-5.2 or 5.3-Codex
Frontend/UI → GPT-5
Massive review → Claude Opus 4.6 Agent Teams
Rapid prototyping → Gemini 3 Flash

Question 2: What's your budget?

< $50/month → Gemini 3 Flash + GPT-5 (occasionally)
$50-500/month → Claude Sonnet 4.5 daily + Opus 4.5 (critical)
$500+/month → GPT-5.3-Codex + Claude Opus 4.6 Agent Teams

Question 3: What's your tech stack?

Python-only → Claude Opus 4.5 (81.6% SWE-bench)
Multi-language → GPT-5.3-Codex (64.7% SWE-Pro)
Windows/.NET → GPT-5.2 or 5.3-Codex
React frontend → GPT-5 (design) + Claude Opus (architecture)

Question 4: Do you have specific needs?

Absolute confidentiality → DeepSeek R1 (self-hosted)
Cybersecurity research → GPT-5.3-Codex (restricted access)
Huge context (1M tokens) → Claude Opus 4.6
Open-source → DeepSeek R1

Our Byrnu Recommendation

For 80% of developers, the optimal stack is:

Claude Sonnet 4.5: Daily use (30h/week)
- Price: $3/$15 per M tokens
- Performance: 77.2% SWE-bench Verified
- Context: 200k tokens
- Justification: Best price/performance/quality balance
GPT-5: Frontend, UI/UX, apps from scratch
- Price: $1.25/$10 per M tokens
- Justification: Aesthetic design + creativity
Gemini 3 Flash: Prototyping, scripts, CI/CD
- Price: $0.075/$0.30 per M tokens
- Justification: 80x cheaper, decent performance

Total estimated cost: $30-100/month for 30h of assisted coding (ROI: 5x-10x).

For teams with advanced needs, add:

GPT-5.3-Codex: DevOps, automation, refactoring
Claude Opus 4.5: Critical Python GitHub issues

Additional cost: $200-800/month (ROI: 10x-50x on specific tasks).

Resources

Next update: March 2026 (after the rumored GPT-5.4-Codex and Claude Opus 5.0).

Have feedback on these models? Contact us or share on our LinkedIn.

This article is part of our AI-Assisted Development (AIAD) series.