TokenMix Research Lab · 2026-05-22

Frontier Pro Tier 2026: GPT-5.5 vs Claude Opus 4.7 vs Gemini 3.x

Last verified: 2026-05-22 Author: TokenMix Research Lab

The three frontier Pro tier API models — OpenAI GPT-5.5, Anthropic Claude Opus 4.7, and Google Gemini 3.1 Pro Preview — sit at fundamentally different price-performance points. GPT-5.5 leads agentic coding (Terminal-Bench 2.0: 82.7%), Claude Opus 4.7 leads pure code resolution (SWE-Bench Pro: 64.3% — memorization caveat per OpenAI), and Gemini 3.1 Pro is cheapest on input ($2/$12 below 200K, $4/$18 above) while all three support 1M-class context. No single Google, OpenAI, or Anthropic page compares all three because each vendor only documents itself. This page synthesizes them from the three official pricing and benchmark sources, with explicit caveats for vendor-reported benchmarks and tokenizer differences.

Table of Contents

• Quick Verdict: Which Pro Model for Which Workload
• The Three Frontier Pro Models Today
• Pricing Across Standard, Batch, and Cache Hit Tiers
• Coding Benchmarks: Terminal-Bench, SWE-Bench, ARC-AGI
• Math and Reasoning Benchmarks
• Long Context Handling
• Cost Per Task: Three Realistic Workloads
• Decision Matrix: When to Pick Each Model
• FAQ
• Sources
• TokenMix Take

Quick Verdict: Which Pro Model for Which Workload

Three different "best" answers depending on what you optimize for. GPT-5.5 wins on agentic coding throughput and tool use; Claude Opus 4.7 wins on code resolution accuracy and prompt caching economics; Gemini 3.1 Pro Preview wins on input cost and context length. None of them dominates all dimensions — picking incorrectly costs 2-5× on token bill for the same workload.

The Three Frontier Pro Models Today

All three are accessible via API as of 2026-05-22, but their status labels differ in meaningful ways. GPT-5.5 ships as the standard production tier on OpenAI. Claude Opus 4.7 is Anthropic's latest Opus generation. Gemini 3.1 Pro Preview remains in Preview status — Google has not promoted any Gemini 3.x Pro model to Stable.

Two structural notes: (a) All three Pro models support 1M-class context, but the cost profile differs. Anthropic explicitly states that "Opus 4.7, Opus 4.6, and Sonnet 4.6 include the full 1M token context window at standard pricing. (A 900k-token request is billed at the same per-token rate as a 9k-token request.) Prompt caching and batch processing discounts apply at standard rates across the full context window." — caching is an optional discount, not a required access mechanism. OpenAI applies a context-tier pricing breakpoint at 272K input tokens. Google applies a breakpoint at 200K input tokens. (b) Gemini 3.1 Pro Preview is still Preview status — Google's published guidance is that Preview models can change without notice. Treat this as a risk factor for production SLA commitments.

Pricing Across Standard, Batch, and Cache Hit Tiers

Per-million-token Standard tier prices, verified 2026-05-22 on each vendor's official page. Each provider exposes different tier modifiers — Batch discounts on OpenAI and Google, Cache-hit discounts on Anthropic — so apples-to-apples comparison requires looking at the modifier landscape, not just headline rates.

Standard Tier

Tier Modifiers

The cache economics dominate for production workloads with stable prefixes (system prompt + retrieved context). Opus 4.7's cache-hit pricing makes it the cheapest Pro tier on shared-prefix workloads despite a higher base rate. Gemini 3.1 Pro wins on cold input where no cache exists. Important caveat for Opus 4.7 cost projections: Anthropic documents that the Opus 4.7 tokenizer may use up to 35% more tokens for the same fixed text — multiply pure per-token comparisons by ~1.35 when comparing Opus 4.7 spend to GPT-5.5 or Gemini.

Coding Benchmarks: Terminal-Bench, SWE-Bench, ARC-AGI

Coding benchmark leadership is split. GPT-5.5 leads agentic coding (multi-step tool use), Claude Opus 4.7 leads pure code resolution from natural-language issue descriptions, and Gemini 3.1 Pro trails on both. The gap depends on workload type, not "which model is smarter overall."

Source: OpenAI GPT-5.5 launch post reports cross-vendor scores. OpenAI explicitly flagged that SWE-Bench Pro Public has known memorization issues — treat that number with caution across all three vendors.

Practical reading: if your workload is "execute multi-step shell + code commands in a loop," route to GPT-5.5. If your workload is "given a GitHub issue, produce a patch," Claude Opus 4.7 is the lead. ARC-AGI scores predict abstract reasoning behavior more than developer-workflow performance.

Math and Reasoning Benchmarks

Math benchmark differences only matter for STEM and tool-use research workloads. For typical coding or business automation tasks, the GPQA Diamond gap of less than 1 percentage point does not predict real-world quality differences.

Source: OpenAI GPT-5.5 launch post benchmark tables. FrontierMath Tier 4 is the steepest reasoning benchmark currently published — GPT-5.5's 35.4% vs Gemini 3.1 Pro's 16.7% is a 2× gap, which is the most material multi-vendor benchmark difference in current frontier Pro tiers.

Long Context Handling

All three Pro models support 1M-class context windows, with three different cost structures. Anthropic explicitly states Opus 4.7 includes the full 1M context window at standard per-token pricing — a 900K request is billed at the same rate as a 9K request, with no required caching to access the full window. OpenAI applies a context-tier pricing breakpoint at 272K input tokens (≤272K is $5/$0.50 cache/$30; >272K doubles to $10/$1.00 cache/$45). Gemini applies a tier breakpoint at 200K input ($2/$12 → $4/$18 above 200K).

For workloads above 200K input, all three models remain viable. Opus 4.7 accesses the full 1M window at flat per-token pricing without requiring caching (caching is an optional discount, not a requirement). Gemini stays cheapest on cold input (>200K tier $4/$18 still well below GPT-5.5's >272K $10/$45). Opus 4.7 is the cheapest option if your workload has a stable cacheable prefix. The real differentiator at long context is benchmark performance, which the three vendors do not publish in apples-to-apples form.

Cost Per Task: Three Realistic Workloads

Same workload routed to each model produces 2-4× cost spread. Below are three realistic patterns with explicit math, all computed from verified 2026-05-22 standard tier pricing.

Scenario 1: Codex-style bug fix (30K input / 8K output)

GPT-5.5:           30K × $5 + 8K × $30  = $0.15 + $0.24 = $0.39
Claude Opus 4.7:   30K × $5 + 8K × $25  = $0.15 + $0.20 = $0.35
Gemini 3.1 Pro:    30K × $2 + 8K × $12  = $0.06 + $0.10 = $0.16

Gemini 3.1 Pro is 59% cheaper than the average of GPT-5.5 and Opus 4.7 for this profile. Opus 4.7 is 10% cheaper than GPT-5.5 at this size, mostly from the $25 vs $30 output gap.

Scenario 2: Long-context document review (800K input / 4K output)

GPT-5.5:           Above 272K boundary — OpenAI long-context rate $10 input / $45 output
                   800K × $10 + 4K × $45 = $8.00 + $0.18 = $8.18
                   (with cache hit on the input prefix: down to ~$1.00/MTok cache input)
Claude Opus 4.7:   Full 1M at standard pricing — 800K × $5 + 4K × $25 = $4.00 + $0.10 = $4.10
                   (with cache hit on input prefix: 800K × $0.50 + 4K × $25 = $0.40 + $0.10 = $0.50 effective)
                   Apply +35% tokenizer caveat to compare same-text spend vs GPT-5.5 / Gemini
Gemini 3.1 Pro:    Above 200K boundary — 800K × $4 + 4K × $18 = $3.20 + $0.072 = $3.27
                   (with cache hit on >200K tier: 800K × $0.40 + 4K × $18 = $0.32 + $0.072 = $0.39 effective)

Gemini 3.1 Pro is the lowest cold-input cost at this scale. Opus 4.7 is not excluded at 800K — full 1M window is included at standard pricing. With cache hits on stable prefixes, Opus 4.7 and Gemini 3.1 Pro both drop below $0.50 per 800K-token request. GPT-5.5 is the most expensive once the >272K tier kicks in, regardless of caching.

Scenario 3: Bulk summarization (200M input / 50M output, monthly, Batch tier)

GPT-5.5 Batch:               200M × $2.50 + 50M × $15 = $500 + $750 = $1,250
Claude Opus 4.7 Batch:       200M × $2.50 + 50M × $12.50 = $500 + $625 = $1,125
                              (50% off standard per Anthropic docs)
Gemini 3.1 Pro Batch (≤200K): 200M × $1 + 50M × $6 = $200 + $300 = $500

Gemini 3.1 Pro Batch is 60% cheaper than GPT-5.5 Batch and ~55% cheaper than Opus 4.7 Batch for the same throughput. For batch-tolerant workloads, Gemini is the default. Opus 4.7 Batch is now public (Anthropic pricing) — earlier versions of this page misstated this as undisclosed; fact verified 2026-05-22.

Decision Matrix: When to Pick Each Model

FAQ

Which is cheapest: GPT-5.5, Claude Opus 4.7, or Gemini 3.1 Pro?

Gemini 3.1 Pro Preview is cheapest on standard input ($2/MTok ≤200K), while Claude Opus 4.7 becomes cheapest when prompt caching applies ($0.50/MTok cache hit). GPT-5.5 is the most expensive standard tier per input token at $5/MTok across all context sizes. Verified 2026-05-22.

Which is best for agentic coding?

GPT-5.5 leads Terminal-Bench 2.0 at 82.7%, ahead of Claude Opus 4.7 (69.4%) and Gemini 3.1 Pro (68.5%) per the OpenAI launch post. This benchmark measures multi-step shell-and-code workflows.

Which is best for solving GitHub issues?

Claude Opus 4.7 leads SWE-Bench Pro Public at 64.3%, ahead of GPT-5.5 (58.6%) and Gemini 3.1 Pro (54.2%). OpenAI flagged memorization concerns with this benchmark, so treat the gap as directional, not definitive.

Which has the largest context window?

All three Pro models include 1M-class context at their standard per-token rates. Anthropic explicitly states "Opus 4.7... include the full 1M token context window at standard pricing. (A 900k-token request is billed at the same per-token rate as a 9k-token request.)" — no caching is required to access the full window. GPT-5.5 and Gemini 3.1 Pro Preview also support 1M input tokens directly. For long-document workloads, all three are viable.

Does Gemini 3.1 Pro have tier pricing?

Yes. Per Google's pricing page, Gemini 3.1 Pro Preview charges $2/$12 per MTok for prompts ≤200K input tokens, then doubles to $4/$18 for prompts above 200K. This is unique among the three — GPT-5.5 and Opus 4.7 use flat per-token rates regardless of context.

Does Claude Opus 4.7 have a Batch tier?

Yes. Anthropic's pricing page states the Batch API offers a 50% discount on both input and output tokens. For Claude Opus 4.7, this is $2.50/MTok input and $12.50/MTok output at the Batch tier. Cache-hit pricing at $0.50/MTok input can stack additionally for prompt-prefix-stable workloads.

Is Gemini 3.1 Pro Preview production-ready?

Google labels it "Preview" status, meaning the API may change without notice. For production SLA commitments, this is a non-trivial risk. GPT-5.5 and Claude Opus 4.7 are GA-status models from their respective vendors.

What about cache pricing differences?

All three vendors publicly disclose cache-hit pricing. GPT-5.5 cache hit input is $0.50/MTok at ≤272K context and $1.00/MTok at >272K context per OpenAI pricing. Claude Opus 4.7 cache hit is $0.50/MTok flat across the full 1M context window (90% off the $5 base). Gemini 3.1 Pro cache hit ranges $0.20-$0.40/MTok depending on context tier.

How do I pick between these three for my workload?

Map your workload to one of three buckets: (a) agentic coding → GPT-5.5; (b) issue-to-patch coding → Claude Opus 4.7; (c) long-context or cost-sensitive → Gemini 3.1 Pro. Then run an eval suite on the top candidate; for high-stakes workloads, test the top two and compare on real prompts.

How frequently do these prices change?

OpenAI, Anthropic, and Google have each adjusted Pro tier pricing at least once in the 12 months ending May 2026. The current numbers above are verified 2026-05-22; for spend planning beyond 3 months, treat them as indicative and re-verify before commitment.

Are there any cost factors beyond per-token rates?

Yes — three significant ones. (1) Opus 4.7's new tokenizer uses up to 35% more tokens for the same fixed text per Anthropic docs, so per-token comparisons understate Opus spend by ~1.35×. (2) GPT-5.5 charges a separate tier above 272K input tokens — model name in OpenAI's table literally reads gpt-5.5 (<272K context length) indicating tier boundary. (3) Gemini 3.1 Pro Preview doubles input price above 200K from $2 to $4 per MTok. Cross-vendor per-task cost requires accounting for all three of these tier mechanics.

Where do I find official pricing for all three?

OpenAI: platform.openai.com/docs/pricing. Anthropic: docs.claude.com/en/docs/about-claude/pricing. Google: ai.google.dev/gemini-api/docs/pricing. Each vendor only documents its own pricing.

Can I switch between them without major code changes?

Yes if you abstract the model behind a config flag. OpenAI's SDK, Anthropic's SDK, and Google's Gemini SDK all accept similar parameters (model name, messages, temperature, max tokens) though concrete differences exist around tool calling, structured output, and streaming. OpenAI-compatible gateways reduce switching cost further.

Sources

All facts on this page sourced from vendor-owned official pages, verified 2026-05-22:

• OpenAI — Pricing documentation (GPT-5.5 / GPT-5.5 Pro standard and tier modifiers)
• OpenAI — GPT-5.5 launch post (cross-vendor benchmarks reported by OpenAI)
• Anthropic — Claude pricing documentation (Opus 4.7 base input, cache hit, cache write rates)
• Google AI — Gemini API pricing (Gemini 3.1 Pro Preview Standard, Batch, Flex tiers and tier breakpoint at 200K)
• Google AI — Gemini API models (model IDs and status labels)

TokenMix Take

Editorial section. TokenMix research team's interpretation of the cross-vendor comparison above.

The single most actionable pattern from this comparison: route by workload type, not vendor allegiance. A development team using only GPT-5.5 for every task is overpaying ~3× on long-context RAG (where Gemini 3.1 Pro Preview is the lowest-cost option), and a team using only Claude Opus 4.7 is excluded from any >200K context workload entirely. The cost-per-task math in Scenarios 1-3 above demonstrates 2-4× spread across realistic patterns.

The friction in implementing per-workload routing is operational, not technical: three separate API accounts, three separate billing surfaces, three separate rate-limit dashboards. TokenMix.ai exposes all three Pro tier models through one OpenAI-compatible endpoint with unified billing, so per-workload routing becomes a model-string change rather than a multi-vendor procurement cycle. This page will be PUT-updated within 7 days whenever any of the three official pricing pages change.