Released: January 2026
Context Window: 200,000 tokens (~150,000 words)
Architecture: Dense transformer with Constitutional AI training
Position: Anthropic’s flagship reasoning and coding model

Claude Opus 4.5 delivers the industry’s highest verified SWE-bench score at 80.9%. At $5 per million input tokens, it costs 8x more than budget alternatives—but that premium buys the best reasoning quality for complex software engineering and safety-critical applications.

Who this is for: Teams where errors are expensive, researchers needing maximum reasoning depth, and enterprises requiring SOC 2 compliance.

The bottom line: You’re paying 5-8x more for a 4-5 point SWE-bench improvement. Only makes sense when error costs exceed API costs.


Key Capabilities

Extended Thinking Mode

Opus 4.5 can engage deeper reasoning chains for complex problems. When enabled, the model performs more thorough analysis—critical for architectural decisions and safety-critical reasoning. Adds latency but improves accuracy on hard tasks.

Constitutional AI & Safety

Anthropic’s safety training reduces harmful outputs and improves calibration. Less likely to hallucinate on critical tasks, making it the default choice for healthcare, financial compliance, and legal analysis.

Enterprise Trust

SOC 2 Type II certified with HIPAA BAA availability. Zero data retention for enterprise agreements. Available via AWS Bedrock, Google Vertex AI, and Azure AI Foundry.


Benchmarks

BenchmarkScoreContext
SWE-bench Verified80.9%Software engineering tasks (source)
Context Window200K tokens~150,000 words (source)

Comparison: Opus 4.5’s 80.9% SWE-bench leads all models—4.1 points ahead of Kimi k2.5 (76.8%) and 2.9 points ahead of Gemini 3 Flash (78.0%). GPT-5.2 trails at 80.0%.

Note: Anthropic does not publish MMLU or GPQA scores, focusing on software engineering benchmarks.


Pricing

API Pricing

Usage TypePrice per 1M tokens
Input$5.00
Output$25.00
Batch (50% discount)$2.50 input / $12.50 output

Cost comparison: Opus 4.5 costs 8x more than Kimi k2.5 or Gemini 3 Flash ($3/1M). A 500K output session costs $12.50 vs $1.50 with budget alternatives.

Subscription Plans

PlanMonthly CostOpus 4.5 MessagesBest For
Pro$20~100Individual developers, light usage
Max-5x$100~500Small teams, daily workflows
Max-20x$200~2,000Heavy users, enterprise workloads

Break-even: 100 typical messages (10K input + 2K output each) costs $2,500 at API pricing—making the Pro plan ($20) a 99% savings.


Free Access

Important: Claude Opus 4.5 does not offer a free tier. No trial credits or free API access.

Alternatives to evaluate before subscribing:

If you don’t need that final 3-4% of reasoning performance, start with free alternatives.


When to Choose Opus 4.5

Choose Opus 4.5 when:

  • Maximum reasoning quality is critical — That 4% SWE-bench gap matters for safety-critical systems
  • Error costs exceed API costs — Healthcare, financial compliance, legal analysis
  • Enterprise compliance required — SOC 2, HIPAA BAA, zero data retention
  • Already in Claude ecosystem — Using Claude Code or Max plan
  • Reputation is on the line — Customer-facing features or published research

Consider alternatives when:

  • Budget mattersKimi k2.5 delivers 95% capability at 1/8th the cost
  • High-context workflowsGemini 3 Flash offers 1M context (5x larger)
  • Need cached pricing — GPT-5.2 Pro offers 90% discount on repeated context
  • Exploratory work — Start with free tiers

See Premium Tier LLM Comparison for head-to-head analysis with GPT-5.2 Pro.


Limitations

No cached pricing: Unlike GPT-5.2 Pro’s 90% discount on repeated context, Opus 4.5 offers no caching.

Context size: 200K tokens vs Gemini 3 Flash’s 1M (5x larger) and GPT-5.2’s 400K (2x larger).

Rate limits: Entry-tier API keys have conservative TPM limits.

Safety filters: Occasionally over-refuses on edge cases.

Price premium: At $25/1M output, large requests can cost hundreds. Budget alternatives offer 95% capability at 12% the cost.


Free Access Guides:

Paid Options:

Terms:


Last updated: January 30, 2026. Pricing from Anthropic. Verify before large workloads.