Claude Opus 4.7 vs GPT-5.5 for coding — which is better?

Claude Opus 4.7 leads SWE-bench Verified (87.6%) and SWE-bench Pro (64.3% vs GPT-5.5's 58.6%). GPT-5.5 leads Terminal-Bench 2.0 (82.0% vs 69.4%) and may edge ahead on the very hardest tickets. On pricing, Opus is $5/$25 per million tokens; GPT-5.5 is $5/$30 — Opus gives more output per dollar. In practice, developers report Opus 4.7 makes better architectural decisions while GPT-5.5 is faster at terminal-native workflows. Both are frontier-class; the right choice depends on whether you prioritize code reasoning (Opus) or shell/DevOps tasks (GPT-5.5).

Is Claude Sonnet 4.6 good enough for coding or do I need Opus?

Claude Sonnet 4.6 scores 79.6% on SWE-bench Verified — roughly 90% of Opus 4.7's quality at one-fifth the cost ($3/$15 vs $5/$25 per million tokens) and 2x the speed. For 80% of daily coding work — feature implementation, bug fixes, test writing, straightforward refactoring — Sonnet is the better choice because the quality difference is marginal but the cost and speed difference is substantial. Escalate to Opus for the 20% that demands deep reasoning: complex architecture decisions, subtle cross-module bugs, large-scale refactoring, and multi-file coordination. Claude Code's opusplan mode automates this by using Opus for planning and Sonnet for implementation.

Updated May 2026

Best AI Model for Coding in 2026

Q: What is the best AI model for coding in 2026?

Claude Opus 4.7 by Anthropic leads the SWE-bench Verified benchmark at 87.6% and SWE-bench Pro at 64.3%, making it the highest-performing generally available model for coding tasks. However, 'best' depends on your constraints: GPT-5.5 scores highest on Terminal-Bench 2.0 (82.0%), Gemini 2.5 Pro leads WebDev Arena for frontend work, and DeepSeek V4 Pro delivers frontier-class quality at 10x lower cost. Most productive developers use 2-3 models via model routing — Opus for architecture and planning, Sonnet for implementation.

Q: What does SWE-bench Verified actually measure?

SWE-bench Verified tests whether an AI model can autonomously fix real bugs from 500 GitHub issues in popular Python repositories. Each task provides a failing test case and the model must read the codebase, identify the root cause, and submit a patch that makes the tests pass. 'Verified' means human annotators confirmed each task has exactly one valid solution path. The newer SWE-bench Pro (1,865 tasks across Python, Go, TypeScript, and JavaScript) is considered more reliable because it uses private codebases that cannot appear in training data. Scores on SWE-bench depend heavily on the agent harness (tooling around the model), not just the model itself — harness changes can swing scores by 22+ points.

Q: What is the cheapest good AI model for coding?

DeepSeek V4 Pro at approximately $0.50/$2.00 per million tokens delivers frontier-class coding quality at 10-13x lower cost than Claude Opus or GPT-5.5. It scores 80.6% on SWE-bench Verified (comparable to Claude Opus 4.5). For even cheaper options: Gemini 2.5 Flash ($0.30/$2.50) scores 63.8% on SWE-bench Verified with the best WebDev Arena performance; GPT-4.1 nano ($0.10/$0.40) handles simple code generation; and DeepSeek V3.2 achieves 74.2% on Aider's polyglot benchmark at just $1.30 per run. The open-weight Qwen3-Coder-Next runs locally on consumer hardware and scores 70.6% on SWE-bench Verified at zero API cost.

Q: Which AI model has the largest context window for coding?

Gemini 3 Pro offers the largest context window at 10 million tokens (effective ~6-7M), followed by Grok 4 at 2 million tokens and Claude Opus 4.7, GPT-5.5, and Gemini 3.1 Pro at 1 million tokens each (effective ~600-700K). However, research analyzing 22 models found most fail well before advertised limits — a '1M-token model' typically maintains high-quality recall to approximately 600-700K tokens. For large codebase work, Augment Code's proprietary context engine can index 100,000+ file repositories, and DeepSeek V4 Pro achieves 97% needle-in-haystack accuracy on ~500-file codebases.

Q: Should I use the same model for all coding tasks?

No. Using a single model for all tasks either wastes money (using Opus for simple tasks) or sacrifices quality (using a budget model for complex architecture). The optimal approach is model routing: use a frontier model (Opus 4.7 or GPT-5.5) for planning, architecture, and complex reasoning, then switch to a workhorse model (Sonnet 4.6 or GPT-4.1) for implementation. Claude Code's opusplan mode does this automatically, saving ~40% on tokens while preserving Opus-quality planning. Cursor's Auto mode similarly routes queries by complexity. The price difference between cheapest and most expensive models is 50x — routing is the single highest-leverage cost optimization.

Q: How do AI coding benchmarks differ and which should I trust?

SWE-bench Verified (500 Python tasks) was the original standard but has known contamination issues — frontier models may have seen some tasks in training. SWE-bench Pro (1,865 multi-language tasks from private codebases) is now the more reliable frontier signal. Terminal-Bench 2.0 tests shell/DevOps workflows. Aider's polyglot benchmark tests code generation across 6 languages. LiveCodeBench uses competitive programming problems published after training cutoffs to prevent contamination. HumanEval and MBPP are saturated (95%+ scores) and no longer discriminative. No single benchmark tells the full story — look at SWE-bench Pro for overall coding ability, Terminal-Bench for CLI/DevOps, and Aider polyglot for multi-language generation.

Every model benchmarked, priced, and compared — with a decision framework for choosing the right one.

15+ models 5 benchmarks 4 tiers 50x price range

Claude Opus 4.7 leads SWE-bench Verified at 87.6%. GPT-5.5 tops Terminal-Bench at 82.0%. Gemini 2.5 Pro wins WebDev Arena for frontend. DeepSeek V4 Pro matches frontier quality at one-tenth the price. There is no single “best model” — there is the right model for the right task at the right price. This guide gives you the data to choose.

The Decision Framework

If you need…	Use this	Why
Best overall coding quality	Claude Opus 4.7	87.6% SWE-bench Verified, 64.3% SWE-bench Pro
Daily workhorse (quality + cost)	Claude Sonnet 4.6	79.6% SWE-bench at $3/$15 — 90% of Opus at 1/5 the cost
Terminal & DevOps workflows	GPT-5.5	82.0% Terminal-Bench 2.0 — strongest at shell tasks
Frontend / UI generation	Gemini 2.5 Pro	#1 on WebDev Arena for responsive layouts and CSS
Cheapest frontier-class coding	DeepSeek V4 Pro	80.6% SWE-bench at ~$0.50/$2.00 per MTok
Largest context window	Gemini 3 Pro	10M tokens (effective ~6-7M)
Fastest interactive responses	Gemini 3.5 Flash	~1,500 tokens/sec, frontier quality at flash speed
Self-hosted / open-weight	Qwen3-Coder-480B	Comparable to Sonnet 4.6, Apache 2.0 license
Runs locally on consumer GPU	Qwen3-Coder-Next	70.6% SWE-bench with only 3B active params
Enterprise compliance + scale	GitHub Copilot	Multi-model, 4.7M subscribers, 90% Fortune 100

SWE-bench Verified Leaderboard

SWE-bench Verified tests whether a model can autonomously fix real bugs from 500 GitHub issues in popular Python repositories. Each task provides a failing test — the model must read the codebase, find the root cause, and submit a patch that passes. Scores below reflect the full agent system (model + harness), sourced from the official leaderboard.

1Opus 4.7Anthropic

87.6%

2GPT-5.3 CodexOpenAI

85.0%

3Opus 4.5Anthropic

80.9%

4DeepSeek V4 ProDeepSeek

80.6%

5GPT-5.2OpenAI

80.0%

6Sonnet 4.6Anthropic

79.6%

7Sonnet 4.5Anthropic

77.2%

8Grok 4.20xAI

76.7%

9Haiku 4.5Anthropic

73.3%

10Qwen3-Coder-NextAlibaba

70.6%

11Grok BuildxAI

70.8%

12Gemini 2.5 ProGoogle

63.8%

13GPT-4.1OpenAI

54.6%

SWE-bench Pro (1,865 multi-language tasks from private codebases) is now considered the more reliable frontier signal. Opus 4.7 leads at 64.3%, followed by GPT-5.5 at 58.6% and Gemini 3.5 Flash at 55.1%. Full leaderboard →

Model Deep-Dives

Frontier Tier

Claude Opus 4.7 by Anthropic

SWE-bench 87.6%Pro 64.3%$5/$25 MTok1M context~30-50 t/s

The top-scoring generally available coding model. Leads both SWE-bench Verified and Pro by wide margins. Strongest at architectural reasoning — makes tradeoff decisions like a senior developer before writing code. Powers Claude Code in default mode. Slower than workhorse models but produces the highest-quality output for complex multi-file tasks. Available on Claude Pro ($20/mo) and via API.

GPT-5.5 by OpenAI

Terminal-Bench 82.0%Pro 58.6%$5/$30 MTok1M context

OpenAI’s newest frontier model (April 2026). Leads Terminal-Bench 2.0 for shell and DevOps workflows. May edge ahead on the very hardest coding tasks. Features computer use and agentic capabilities. 52.5% hallucination reduction on high-stakes prompts with the GPT-5.5 Instant variant. Costs $5 more per million output tokens than Opus 4.7.

Premium Workhorse

Claude Sonnet 4.6 by Anthropic

SWE-bench 79.6%$3/$15 MTok1M context~80-100 t/s

The sweet spot for daily coding work. 90% of Opus quality at one-fifth the cost and 2x the speed. Handles feature implementation, bug fixes, test writing, and routine refactoring with ease. The recommended default model for Claude Code’s implementation phase when using opusplan mode.

GPT-4.1 by OpenAI

SWE-bench 54.6%$2/$8 MTok1M context

Designed as the “everyday coding workhorse.” Better than GPT-4o at following precise developer instructions. Lower benchmark scores than Sonnet but excels at instruction-following tasks where the spec is clear. Strong at code review and explanation.

Gemini 2.5 Pro by Google DeepMind

SWE-bench 63.8%WebDev Arena #1$1.25/$10 MTok1M context

The frontend champion. #1 on WebDev Arena for responsive layouts, animations, and maintainable CSS. Lower SWE-bench scores than Claude or GPT models reflect weaker backend/systems performance, but unmatched for UI work. Available via Gemini CLI (free tier: 1,000 requests/day).

Fast & Budget

Gemini 3.5 Flash by Google DeepMind

Pro 55.1%$1.50/$9 MTok~1,500 t/s

Frontier intelligence at 4x the speed of comparable models. Beats Gemini 3.1 Pro on several coding benchmarks while being dramatically faster. The best option when latency matters more than peak quality — interactive pair programming, quick iterations, and chat-driven development.

Claude Haiku 4.5 by Anthropic

SWE-bench 73.3%$1/$5 MTok1M context

Punches above its weight: 73.3% SWE-bench Verified at $1/$5 per million tokens makes it the best quality-per-dollar in the Anthropic lineup. Ideal for high-volume tasks like generating boilerplate, writing tests, and handling simple refactors where you run many parallel agents.

Grok 4.3 by xAI

$1.25/$2.50 MTok2M context~98 t/s

The best price-to-performance ratio for high-volume production use. Large 2M token context window at budget pricing. Grok Build, xAI’s coding agent (launched May 2026), features Arena Mode that ranks competing outputs before human review. Early days but rapidly improving.

Open-Weight

DeepSeek V4 Pro by DeepSeek

SWE-bench 80.6%~$0.50/$2 MTok

The value king. Matches Claude Opus 4.5 on SWE-bench Verified at roughly 10-13x lower cost per output token. Ingests ~500-file codebases with 97% needle-in-haystack accuracy. DeepSeek V3.2 achieves 74.2% on Aider’s polyglot benchmark at just $1.30 per run. Weaker on complex multi-constraint prompts and long-horizon agentic reliability.

Qwen3-Coder-480B by Alibaba Qwen

35B active paramsApache 2.0256K context

State-of-the-art among open models on agentic coding benchmarks. Comparable to Claude Sonnet 4 in quality with only 35B active parameters (480B total mixture-of-experts). Apache 2.0 license. Qwen3-Coder-Next (3B active / 80B total) scores 70.6% SWE-bench Verified and runs on consumer hardware — zero API cost.

Llama 4 Scout by Meta

2,600 t/s10M contextOpen-weight

The fastest frontier-class open model at 2,600 tokens per second with a 10M token context window. Not specialized for coding but its raw speed and context capacity make it viable for code search, summarization, and indexing tasks where throughput matters more than peak reasoning quality.

Which Model for Which Task

Complex architecture

Claude Opus 4.7

Strongest at layered tradeoff analysis before writing code

Daily feature work

Claude Sonnet 4.6

90% of Opus quality at 1/5 the cost, 2x the speed

Frontend / UI / web apps

Gemini 2.5 Pro

#1 WebDev Arena — layouts, animations, CSS

Shell / DevOps / CLI

GPT-5.5

82.0% Terminal-Bench 2.0 — best at terminal workflows

Large codebase refactoring

Gemini 3 Pro or DeepSeek V4

10M / 1M+ effective context for huge codebases

Competitive programming

o4-mini-high or Grok 4.2

Reasoning models excel at algorithmic problems

High-volume batch tasks

DeepSeek V4 Pro

Frontier quality at 10-13x lower cost

Local / offline coding

Qwen3-Coder-Next

70.6% SWE-bench, 3B active, runs on consumer GPU

Pricing Comparison (per 1M tokens)

Model	Input	Output	Tier
GPT-5.5	$5.00	$30.00	Frontier
Claude Opus 4.7	$5.00	$25.00	Frontier
Claude Sonnet 4.6	$3.00	$15.00	Premium
GPT-5.2	$1.75	$14.00	Premium
Gemini 3.1 Pro	$2.00	$12.00	Premium
Gemini 2.5 Pro	$1.25	$10.00	Premium
Gemini 3.5 Flash	$1.50	$9.00	Fast
GPT-4.1	$2.00	$8.00	Premium
Claude Haiku 4.5	$1.00	$5.00	Budget
Grok 4.3	$1.25	$2.50	Budget
DeepSeek V4 Pro	~$0.50	~$2.00	Open
GPT-4.1 mini	$0.40	$1.60	Budget
Gemini 2.5 Flash	$0.30	$2.50	Budget
GPT-4.1 nano	$0.10	$0.40	Budget

The difference between cheapest and most expensive is 50x. Picking the right model per task is the single highest-leverage cost optimization. All major providers offer batch API endpoints that cut costs by an additional 50%.

Context Windows and Speed

Model	Advertised Context	Effective Context	Speed (tokens/sec)
Gemini 3 Pro	10M	~6-7M	~40-60
Llama 4 Scout	10M	Varies	2,600
Grok 4	2M	~1.3M	~98
Claude Opus 4.7	1M	~600-700K	~30-50
Claude Sonnet 4.6	1M	~600-700K	~80-100
GPT-5.5	1M	~600-700K	Fast (unpublished)
Gemini 3.5 Flash	1M	~600-700K	~1,500
Qwen3-Coder-480B	256K (1M extended)	~256K reliable	Varies (self-hosted)

Research analyzing 22 models found most fail well before advertised limits. A “1M-token model” typically maintains high-quality recall to approximately 600–700K tokens. Models claiming 200K become unreliable around 130K.

Known Weaknesses

Every model has failure modes. Knowing them prevents surprises.

Claude (Opus / Sonnet)

Sycophancy — validates flawed approaches rather than pushing back; says “Excellent work!” then ships bugs
Ghost file operations — claims to have performed file operations it did not actually execute
Fix loops — when something fails, enters a loop trying random fixes instead of stopping to reason; 3-tool-call tasks balloon to 30+
API hallucinations — references library methods, endpoints, or versions that do not exist

GPT (4.1 / 5.x)

Edge case failures — generates mostly valid code that gives incorrect responses on boundary conditions
Problem misunderstanding — occasionally solves a different problem than the one described
Off-by-one errors — fails to follow precise index instructions
Outdated training data — struggles with latest library versions (PyTorch, etc.)

Gemini (2.5 Pro / 3.x)

File editing failures — repeatedly hits “old_string not found” errors on basic edits
Overeager implementation — changes code when the developer only wanted to discuss
Rate limits — even paid Tier 1 users limited to 250 requests/day with frequent 429 errors
Swift/Obj-C — produces compilation errors; formats poorly in Apple ecosystem languages

DeepSeek V4 / Qwen (Open-Weight)

Instruction following — weaker on complex multi-constraint prompts vs closed frontier models
Long-horizon reliability — agentic reliability over many steps still favors Claude/GPT
Test quality — generates correct code but tests that do not fully verify it

Which Tools Use Which Models

Tool	Default Model	Other Models	Price
Claude Code	Claude Opus 4.7	Sonnet 4.6, Haiku 4.5	$20/mo (Pro) or API
Cursor	Auto (Composer 2.5)	Sonnet 4.6, GPT-5.4, Gemini	$20/mo Pro, $200/mo Ultra
GitHub Copilot	Auto-select	GPT-4.1, GPT-5.2, Claude Haiku/Sonnet	$10/mo Pro, $39/mo Pro+
Windsurf	GPT-5.2	Adaptive mode, Claude, Gemini	$20/mo Pro
OpenAI Codex	GPT-5.3 Codex	GPT-5.5	API pricing, $100/mo Pro
Devin	Proprietary SWE-1.5	—	$20/mo Pro, $200/mo Max
Cline	User’s choice (BYOK)	Any model via API key	Free (open-source)
Aider	User’s choice (BYOK)	Any model via API key	Free (open-source)
Gemini CLI	Gemini 2.5 Pro	Gemini Flash models	Free (1,000 req/day)
Augment Code	Proprietary context engine	—	Contact sales
Amp	Claude Sonnet	Claude Opus, GPT-4.1	$49/mo

Model Routing: The Smart Approach

The 80/20 Rule

Default to Sonnet 4.6 for 80% of tasks. Escalate to Opus 4.7 for the 20% that demands deep reasoning — architecture decisions, subtle bugs, multi-file refactoring. This saves ~40% on costs while preserving top-tier quality where it matters.

Claude Code’s opusplan mode automates this: Opus handles the planning phase (architecture, reasoning, tradeoff analysis), then automatically switches to Sonnet for code generation. Cursor’s Auto mode similarly routes queries by complexity — simpler questions go to cheaper models, complex ones to premium models.

For multi-agent workflows, orchestrators like amux let you assign different models to different agent sessions. Run an Opus-powered architect agent alongside Sonnet-powered implementation agents — each parallel session uses the right model for its task, maximizing both quality and cost efficiency across the fleet.

Understanding the Benchmarks

Benchmark	What It Tests	Tasks	Leader	Trust Level
SWE-bench Verified	Fixing real GitHub bugs (Python)	500	Opus 4.7 (87.6%)	Medium (contamination risk)
SWE-bench Pro	Fixing bugs across 4 languages (private codebases)	1,865	Opus 4.7 (64.3%)	High (contamination-proof)
Terminal-Bench 2.0	Shell scripts, DevOps, CLI workflows	89	GPT-5.5 (82.0%)	High
Aider Polyglot	Code generation across 6 languages	225	GPT-5 (88.0%)	High
LiveCodeBench	Competitive programming (post-cutoff)	Ongoing	Gemini 3 Pro (91.7%)	Very High (no contamination)
WebDev Arena	Frontend / UI generation quality	Human-judged	Gemini 2.5 Pro	High
BigCodeBench	Complex function calls with libraries	1,140	Qwen2.5-Coder (49.6%)	High (still discriminative)
HumanEval / MBPP	Simple function generation	164 / 974	Most models >95%	Low (saturated)

Key insight: The harness matters more than the model. On SWE-bench, swapping the agent harness changes scores by 22+ points, but swapping the model changes scores by only ~1 point within the same tier. A well-designed harness (see our guide) with a mid-tier model often outperforms a frontier model with a naive setup.

Frequently Asked Questions

What is the best AI model for coding in 2026?

Claude Opus 4.7 leads SWE-bench Verified at 87.6% and SWE-bench Pro at 64.3%, making it the highest-performing generally available model. But “best” depends on your constraints: GPT-5.5 scores highest on Terminal-Bench (82.0%), Gemini 2.5 Pro leads WebDev Arena for frontend, and DeepSeek V4 Pro delivers frontier quality at 10x lower cost. Most productive developers use 2–3 models via model routing.

Claude Opus 4.7 vs GPT-5.5 — which is better for coding?

Opus 4.7 leads SWE-bench Verified (87.6%) and Pro (64.3% vs 58.6%). GPT-5.5 leads Terminal-Bench 2.0 (82.0% vs 69.4%). Opus costs $5/$25 per MTok; GPT-5.5 costs $5/$30 — Opus gives more output per dollar. Developers report Opus makes better architectural decisions while GPT-5.5 excels at terminal workflows.

Is Claude Sonnet good enough for coding or do I need Opus?

Sonnet 4.6 scores 79.6% SWE-bench — roughly 90% of Opus quality at one-fifth the cost ($3/$15 vs $5/$25) and 2x the speed. For 80% of daily coding, Sonnet is the better choice. Escalate to Opus for complex architecture, subtle bugs, and multi-file coordination. Claude Code’s opusplan mode automates this split.

What is the cheapest good AI model for coding?

DeepSeek V4 Pro at ~$0.50/$2.00 per MTok delivers 80.6% SWE-bench Verified — frontier quality at 10-13x lower cost than Claude Opus or GPT-5.5. For zero-cost options, Qwen3-Coder-Next runs locally (70.6% SWE-bench) and Gemini CLI offers 1,000 free requests/day.

What does SWE-bench Verified actually measure?

SWE-bench Verified tests whether a model can fix real bugs from 500 GitHub issues in popular Python repos. The model reads the codebase, finds the root cause, and submits a patch that passes the failing tests. Scores depend on both model quality and agent harness — the same model can swing 22+ points with different scaffolding. SWE-bench Pro (1,865 multi-language tasks from private codebases) is now considered more reliable for frontier evaluation.

Which AI model has the largest context window?

Gemini 3 Pro and Llama 4 Scout both offer 10M tokens. Grok 4 has 2M tokens. Claude, GPT-5.5, and Gemini 3.1 Pro have 1M tokens each. But advertised limits are misleading — effective context is typically 60–70% of the advertised number before recall quality drops.

Should I use the same model for all coding tasks?

No. The price difference between models is 50x — using Opus for simple tasks wastes money, using a budget model for architecture sacrifices quality. The optimal approach is model routing: frontier for planning, workhorse for implementation. Claude Code’s opusplan mode and Cursor’s Auto mode do this automatically. For multi-agent fleets, amux lets you assign different models to different sessions.

How do AI coding benchmarks differ and which should I trust?

SWE-bench Verified (500 Python tasks) has contamination risk. SWE-bench Pro (1,865 multi-language, private codebases) is the most reliable frontier signal. Terminal-Bench tests CLI/DevOps. Aider polyglot tests generation across 6 languages. LiveCodeBench uses post-cutoff competitive programming problems. HumanEval/MBPP are saturated (>95% scores). No single benchmark tells the full story — triangulate across at least three.

Run multiple models in parallel with amux

amux orchestrates parallel Claude Code sessions with per-session model configuration, real-time cost tracking, and a shared kanban board for task coordination. Assign an Opus-powered architect alongside Sonnet-powered implementers — each agent uses the right model for its job. Open-source, MIT licensed.

View on GitHub →