Claude Opus 4.7 API Pricing vs 4.6 Legal Tech Budgeting

Claude Opus 4.7 API pricing vs 4.6 is the procurement question every legal-tech budget owner asked the week of April 16, 2026. Per Anthropic's pricing page, the headline rates are identical to 4.6: $5 per million input tokens and $25 per million output tokens. Procurement teams reading the rate card see no change. The bill rises anyway because Anthropic's new tokenizer counts the same content at 1.0 to 1.35x the previous rate, with legal prose sitting near the high end. Here's the legal-tech budgeting playbook for 2026 with the math broken down by workload, deployment surface, and firm size.

API rate card: 4.7 vs 4.6 head-to-head

Per Anthropic's pricing page as of April 28, 2026:

Opus 4.7 API: - $5 per million input tokens - $25 per million output tokens - Batch processing: 50% off - US-only inference: 1.1x premium

Opus 4.6 (still available): - $5 per million input tokens - $25 per million output tokens

The rate card is identical. What changed:

Tokenizer. 4.7's tokenizer counts the same content at 1.0-1.35x the 4.6 rate, depending on content type. Code-heavy content compresses well (1.0-1.05x). Legal prose with case citations and Latin phrases sits near the ceiling (1.25-1.35x).

Effective price per task. Same prompt produces same answer (or improved answer at xhigh). Token consumption is higher. Per-task spend rises by 15-30% on legal-prose workloads.

Effort levels. xhigh consumes more output tokens per task than 4.6 high. Default migration without effort-level review accidentally raises consumption further.

The tokenizer cost calculator covers the math for your specific content mix. The Opus 4.7 anchor covers the broader change set.

Comparison: Opus 4.7 vs Sonnet 4.6 vs Haiku 4.5

Anthropic's broader model lineup as of April 2026:

Opus 4.7: $5/M input, $25/M output. The most capable model. Use for complex analysis, brief drafting, M&A diligence, agentic workflows.

Sonnet 4.6: $3/M input, $15/M output. Balanced capability and cost. Use for routine legal work, document classification, contract review at scale.

Haiku 4.5: $1/M input, $5/M output. Fastest, cheapest. Use for bulk extraction, classification, simple summarization.

For legal teams, the practical implication: not every task needs Opus 4.7. A 50,000-document first-pass discovery review can run on Sonnet 4.6 at materially lower cost ($3/M input vs $5/M; $15/M output vs $25/M). Reserve Opus 4.7 for the analysis that actually benefits from xhigh effort and multi-session memory.

Worked example for a hybrid model deployment on a 50,000-document discovery production:

- First-pass relevance review on Sonnet 4.6: 30M input + 8M output = $90 + $120 = $210 - Privilege re-review on responsive subset (8,000 docs) on Opus 4.7 at xhigh: 12M input + 5M output = $60 + $125 = $185 - Total: $395

Vs all-Opus 4.7 at high: roughly $625 for the same work. Hybrid deployment saves 35-40% on discovery review cost without sacrificing quality on the privilege determinations that carry malpractice risk. The task budgets discovery spoke covers the budgeting side.

GPT-5.5 API rate card comparison

OpenAI's GPT-5.5 launched April 23, 2026. Per the OpenAI pricing page:

GPT-5.5 API: - $5 per million input tokens - $30 per million output tokens - Cached input: $0.50/M (90% off) - Batch API: 50% off - Tokens above 272K: 2x input / 1.5x output multiplier

GPT-5.5 Pro API: - $30 per million input tokens - $180 per million output tokens - Confirmed by multiple secondary sources (apidog, langcopilot)

Comparison vs Opus 4.7:

- Input rate: GPT-5.5 matches Claude at $5/M - Output rate: GPT-5.5 is $30/M vs Claude at $25/M (20% higher) - Pro tier: GPT-5.5 Pro at $30/$180 is materially more expensive than Opus 4.7 - Cached input: GPT-5.5's cached rate at $0.50/M is aggressive; Anthropic's prompt caching reduces effective input cost similarly on cache hits - Long context: GPT-5.5's 1M-token window has a multiplier above 272K tokens; Opus 4.7's per-request context is smaller but persistent across sessions

For most legal workloads, the per-token economics roughly trade off depending on output-heavy vs input-heavy patterns. Workflow fit (calibration, multi-session memory, vision quality, integration) drives the decision more than per-token cost. The GPT-5.5 calibration and disclosure analysis covers the broader GPT-5.5 picture.

Budget projection by firm size and workload

Solo practitioner with light AI use (10-20 hours/month): - Claude Pro at $20/user/month month-to-month or $17 annual rate ($240 annual at annual rate) - API consumption negligible at this volume - Total annual: $240-300 - Tokenizer impact: zero (flat consumer pricing)

Mid-size firm (25 attorneys, moderate AI integration): - Claude Team at $25/user/month annual rate: $7,500/year - API consumption for internal tooling: $500-2,000/month if applicable - Total annual: $13,500-31,500 depending on consumption - Tokenizer impact on consumption portion: 1.15-1.25x increase ($75-500/month additional)

BigLaw (200 attorneys, heavy AI integration): - Claude Enterprise at $20/user/month annual + usage at API rates: $48,000/year base - API consumption: $20,000-100,000+/month depending on workload and surface - Total annual: $288,000-1,248,000 - Tokenizer impact: full $25,000-150,000+/year increase on consumption portion at same workload

For firms running active Anthropic enterprise contracts with consumption-scaling clauses, the tokenizer change is a material renewal-negotiation factor. Document the change in renewal preparation. The Microsoft Foundry procurement guide covers an alternative deployment surface that may have different unit economics.

Budget mitigation toolkit: how to absorb the increase

Five operational moves that offset 40-60% of the tokenizer-driven cost increase:

1. Hybrid model deployment. Default routine work to Sonnet 4.6 ($3/M input, $15/M output). Reserve Opus 4.7 for tasks that benefit from xhigh and multi-session memory. A 50,000-document review on hybrid deployment runs ~35% cheaper than all-Opus, per the worked example above.

2. Prompt caching. Anthropic's prompt caching reduces effective input cost on cache hits. For long-document workflows that hit the same source repeatedly, caching pays back configuration overhead within the first few queries.

3. Batch API for non-time-sensitive workloads. Per the pricing page, batch processing gives 50% off input tokens. Overnight document classification, summarization batches, and bulk extraction all qualify. For workloads that don't need synchronous responses, batch is a free 50% discount.

4. Right-sized effort levels. xhigh consumes more output tokens than high. Default routine work to high or medium; reserve xhigh for analysis-heavy tasks. The effort levels xhigh when-to-use spoke covers the per-task math.

5. Task budgets on agentic workflows. Predictable consumption is also more negotiable in renewal terms with Anthropic. Set caps on discovery review, contract review at scale, brief drafting workflows. Predictability lowers risk profile.

Applied together, these mitigations typically offset 40-60% of the tokenizer-driven increase for legal-prose-heavy workflows. The exposure is real but manageable.

Procurement decision framework for 2026

A working framework for legal-tech budget owners deciding 2026 spend:

Step 1: Audit current consumption-based AI spend. Pull Q1 2026 actuals across Anthropic, OpenAI, Spellbook, Harvey, CoCounsel, Microsoft Copilot. This is your baseline.

Step 2: Project Q2-Q4 consumption at 4.7 tokenizer multiplier. Apply 1.15-1.25x weighted multiplier to Anthropic consumption portion (based on your content mix). Other vendors are unaffected.

Step 3: Identify mitigation opportunities. Hybrid model deployment, prompt caching, batch API, effort-level optimization, task budgets. Project 40-60% offset on the tokenizer-driven increase.

Step 4: Compare deployment surfaces. claude.ai Enterprise vs AWS Bedrock vs Vertex AI vs Microsoft Foundry. Each has slightly different unit economics, data residency posture, and procurement velocity. The right surface depends on existing IT relationships and compliance requirements.

Step 5: Negotiate consumption contracts on workload, not on inflated 2026 token counts. When negotiating volume commitments or renewal terms, document the tokenizer change as the cause of any consumption growth that doesn't reflect actual workload growth. Otherwise renewal prices off inflated baseline.

Step 6: Update internal cost-recovery rates. If your firm bills Claude consumption back to clients, update the rate card and document the methodology in cost-recovery records. Disclose the change in client communications about rate adjustments.

The Anthropic Legal Ecosystem map covers the broader procurement landscape and vendor consolidation context.

The Bottom Line: The verdict: 4.7 vs 4.6 sticker pricing is identical at $5/M input and $25/M output. Effective per-task cost on legal prose is 15-30% higher because of the new tokenizer. Hybrid model deployment (Sonnet 4.6 for routine, Opus 4.7 for analysis), prompt caching, batch API, right-sized effort levels, and task budgets typically offset 40-60% of the increase. Audit consumption contracts before renewal; document the tokenizer change as the cause of any consumption growth that doesn't reflect workload growth.