Grok 4.5 vs Claude vs ChatGPT (GPT-5.6)
A coding-focused comparison of Grok 4.5 (xAI), Claude (Anthropic), and ChatGPT/GPT-5.6 (OpenAI), evaluating benchmark accuracy, cost per completed task, and agentic coding tool support.
Quick Answer
Claude leads on the hardest coding benchmarks and has the deepest developer-tool ecosystem (Claude Code); Grok 4.5 delivers near-frontier accuracy at a fraction of the cost and token usage; GPT-5.6 is closely competitive on standard coding benchmarks with the strongest agentic command-line tooling via Codex.
Reviewed by TechLogHub Engineering Team. Last updated October 1, 2026. Updated for Grok 4.5's July 8, 2026 launch and GPT-5.6 Sol/Terra/Luna's June 26, 2026 release alongside Claude Opus 4.8/Sonnet 5.
| Feature |  Claude |  ChatGPT (GPT-5.6) | |
|---|---|---|---|
| Developed By | xAI | Anthropic | OpenAI |
| Coding Benchmark (SWE-Bench Pro) | SWE-Bench Pro: 64.7% | SWE-Bench Pro: 69.2% (Opus 4.8) | SWE-Bench Pro: 58.6% (prior GPT-5.5); GPT-5.6 Sol reports 91.9% on Terminal-Bench 2.1 via subagents |
| Pricing (Input/Output per Million Tokens) | $2/$6 per million tokens | $3-$5 / $15-$25 per million tokens (Sonnet 5 / Opus 4.8) | $1-$5 / $6-$30 per million tokens (Luna/Terra/Sol) |
| Token Efficiency (per Task) | ~1.9M tokens per coding task | ~7.2M tokens per coding task (Opus 4.8) | ~6.2M tokens per coding task (GPT-5.5 baseline) |
| Dedicated Coding Tool | Grok Build | Claude Code | OpenAI Codex |
| Context Window | 500K tokens | 1M tokens | 1M tokens |
Grok 4.5
xAI's flagship coding and agentic model, trained in part on data from Cursor, offering near-frontier coding accuracy at roughly a fifth of the cost of its closest rivals.
Pros
- Roughly 5x cheaper per token than Claude and competitive with GPT-5.6's cheapest tier
- Most token-efficient of the three on agentic coding tasks (~1.9M tokens per task vs Claude's ~7.2M)
- Fast task completion in hands-on comparisons — notably quicker on real coding tasks in independent tests
- Deep training lineage with Cursor, one of the most popular AI coding editors
Cons
- Clearly trails on the hardest coding benchmark, SWE-Bench Pro, against both Claude and GPT-5.6
- Highest hallucination rate of the three, a meaningful risk for unreviewed production code
- Newest and least battle-tested of the three in large-scale production use
- Smaller dedicated coding-tool ecosystem compared to Claude Code or Codex
Best For
High-volume, cost-sensitive coding workloads and teams already using Cursor, where near-frontier accuracy at a fraction of the price outweighs owning the single top benchmark score.
Claude
Anthropic's model family, led by Claude Opus 4.8 and Sonnet 5, consistently ranked at or near the top of independent coding benchmarks and human-preference leaderboards.
Pros
- Leads the hardest coding benchmark, SWE-Bench Pro, and tops the LMArena coding leaderboard
- Deepest dedicated coding agent, Claude Code, with sub-agents, MCP support, and headless scripting
- Widely used as the default or preferred model inside third-party AI editors (Cursor, and formerly Windsurf)
- Lower hallucination rate than Grok 4.5 in independent evaluations
- Sonnet 5 offers a much cheaper mid-tier option that closes most of the gap to Opus 4.8
Cons
- Highest per-token pricing of the three at the flagship (Opus) tier
- Uses significantly more tokens to complete equivalent coding tasks than Grok 4.5
- Was briefly inaccessible in June 2026 due to export controls on its top-tier Fable 5 model (Opus 4.8/Sonnet 5 were unaffected)
Best For
Teams where coding accuracy and reliability on the hardest, most complex problems matter most, and who value the maturity of the Claude Code ecosystem.
ChatGPT (GPT-5.6)
OpenAI's GPT-5.6 family (Sol, Terra, Luna), which now powers OpenAI Codex, closely competitive with Claude on standard coding benchmarks and leading on agentic command-line and terminal tasks via its new subagent mode.
Pros
- Near-ties Claude on standard SWE-bench Verified and leads dedicated terminal benchmarks with its subagent mode
- Tiered Sol/Terra/Luna family gives cost/capability flexibility for different coding task sizes
- Codex provides a dedicated desktop command center for multi-agent coding work across projects
- Broadest general-purpose agentic tooling ecosystem, including web browsing and code interpreter
- Widest existing developer familiarity and largest overall user base
Cons
- Still trails Claude on the hardest coding benchmark, SWE-Bench Pro
- Pricier per token than Grok 4.5 at comparable capability
- Full benchmark suite for GPT-5.6's newest tiers was still being published shortly after launch
- Historically less specialized coding-agent ecosystem than Claude Code, despite Codex's rapid improvements
Best For
Teams wanting a strong, broadly capable coding model with the widest general-purpose agentic tooling and the flexibility of a tiered model family for cost control.
Benchmark Accuracy vs Cost-Per-Task
Raw benchmark scores tell only part of the story. Claude Opus 4.8 leads SWE-Bench Pro at 69.2%, but Grok 4.5's 64.7% comes at roughly a fifth of the cost and a quarter of the tokens per task, meaning the effective accuracy-per-dollar picture favors Grok 4.5 for high-volume workloads even though it doesn't top the raw benchmark. GPT-5.6 sits in between on both dimensions, with a tiered pricing structure that lets teams choose their own cost/accuracy trade-off within the same model family.
Agentic Coding Ecosystems
Each provider has built a dedicated agentic coding surface around its models: Claude Code for Anthropic, Codex for OpenAI, and Grok Build for xAI. Claude Code has the deepest agentic primitives (sub-agents, MCP servers, headless scripting) and the longest track record. Codex added a desktop command center for multi-agent work across projects. Grok Build is the newest of the three, benefiting from tight integration with Cursor given the shared training data.
The Hallucination Trade-off
Grok 4.5's accuracy gains came paired with a notable increase in hallucination rate on one widely cited knowledge benchmark (25% to 54%), meaning it's more confident when wrong than either Claude or GPT-5.6. For workflows with human or automated review before code ships, this is manageable; for autonomous, unreviewed agentic pipelines, it's a meaningful risk factor that the raw benchmark numbers don't capture.
Which Model Do Editors Actually Default To?
Claude has become the most commonly preferred default model inside popular third-party AI code editors like Cursor, reflecting real-world developer preference beyond published benchmarks. This ecosystem effect matters: even if a competing model is cheaper or scores close on paper, the accumulated tooling, prompt tuning, and community trust built around Claude in these editors is a real practical advantage that a benchmark table doesn't capture.
Verdict
For raw coding accuracy on the hardest problems, Claude currently leads. For cost-efficiency at near-frontier quality, Grok 4.5 is the clear value pick, provided you have review steps to catch its higher hallucination rate. For the broadest general-purpose agentic tooling alongside strong coding performance, GPT-5.6 is a close, well-rounded competitor. None of the three is a clean universal winner — the right choice depends on whether accuracy, cost, or ecosystem breadth matters most for your workload.