GPT‑5.5 vs. Claude Opus 4.7 vs. China: Who Leads the Field in April 2026?

Between April 7 and April 24, 2026, five labs shipped their strongest models — GLM‑5.1 (April 7), Claude Opus 4.7 (April 16), Kimi K2.6 (April 20), GPT‑5.5 (April 23), and DeepSeek V4‑Pro (April 24). The market shifted more in those three weeks than during the entire first quarter of the year. Anyone building applications on top of these models needs to understand what actually differentiates them — and what doesn't.

This post takes stock: strengths, weaknesses, and real costs. No hype tables — just the perspective of a developer running these models in production.

The Five Models At a Glance

The first important takeaway is in this table: GPT‑5.5 is the only model whose price went up versus its predecessor. Output tokens are 2× more expensive than GPT‑5.4. OpenAI justifies this in its launch post with token efficiency:

"While GPT‑5.5 is priced higher than GPT‑5.4, it is both more intelligent and much more token efficient."

The other four models priced at the same level as their predecessors or below.

Claude Opus 4.7: The Coding Agent Specialist

Anthropic explicitly positioned Opus 4.7 as a focused upgrade over Opus 4.6. No new pricing tier, no architectural revolution — measurable improvements exactly where Opus 4.6 regularly stumbled in production. Vellum captures this in its benchmark analysis:

"This is not a model that sweeps every leaderboard. Anthropic is explicit that Claude Mythos Preview remains more broadly capable. But for developers building production coding agents and long-running workflows, the improvements are real and well-targeted."

The numbers that matter: SWE‑bench Verified jumps from 80.8% to 87.6%. SWE‑bench Pro — the harder multi‑language engineering benchmark — moves from 53.4% to 64.3%. These are not cosmetic shifts; they're values that determine whether an agent finishes a task autonomously or stalls after three steps.

Strengths:

• Coding leadership: 64.3% on SWE‑bench Pro is clearly ahead of GPT‑5.5 (58.6%) and every open Chinese model.
• Vision: images up to 2,576 pixels on the long edge (~3.75 MP), more than 3× the resolution of prior Claude models. CharXiv visual reasoning jumped from 69.1% to 82.1%.
• Literal instruction following: Opus 4.7 takes system prompts more literally than its predecessors. Bullet lists are no longer treated as "optional hints" but as hard requirements.
• MCP‑Atlas: 77.3% to 79.1% across sources — best‑in‑class for real multi‑tool orchestration benchmarks.

Weaknesses:

• Verbosity and latency: On the Artificial Analysis Intelligence Index v4.0, Opus 4.7 (Adaptive Reasoning, Max Effort) scores 57, but generated 110M tokens during the eval run versus a median of 35M. Time‑to‑first‑token is 18.54s.
• Tokenizer inflation: Anthropic shipped a new tokenizer that produces 1.0× to 1.35× more tokens per input depending on content. Finout warns accordingly: > "Do not trust the 35% ceiling as a flat estimate, and do not trust 0% either."
• Multilingual tasks: Gemini 3.1 Pro still leads here.

GPT‑5.5: The Broad Generalist with a Latency Edge

GPT‑5.5 (internal codename "Spud") is, per OpenAI, the first fully retrained base model since GPT‑4.5. The launch page puts it this way:

"On Artificial Analysis's Coding Index, GPT‑5.5 delivers state-of-the-art intelligence at half the cost of competitive frontier coding models."

Strengths:

• Terminal‑Bench 2.0: 82.7% — clearly ahead of Opus 4.7 (69.4%) and DeepSeek V4‑Pro (67.9%).
• BrowseComp: 84.4% (GPT‑5.5 Pro 90.1%) versus 79.3% for Opus 4.7.
• Token efficiency: GPT‑5.5 reaches GPT‑5.4 levels with fewer tokens, per OpenAI.
• "Super‑app" architecture: GPT‑5.5 was explicitly trained for multi‑tool orchestration. OpenAI President Greg Brockman, quoted by TechCrunch: > "It's a faster, sharper thinker for fewer tokens compared to something like 5.4."

Weaknesses:

• Price increase: $5/$30 versus $2.50/$15 for GPT‑5.4 — output doubled.
• SWE‑bench Pro: 58.6% — ~6 points behind Opus 4.7.
• Hallucinations: Tom's Guide ran a 7‑category head‑to‑head between GPT‑5.5 and Opus 4.7. The Wikipedia summary of the tests puts the result soberly: > "GPT-5.5 lost in all 7 categories tested. The website praised GPT-5.5 for its speed but criticized the model for its tendency to hallucinate rather than admitting that it does not know something."
• Delayed API availability: OpenAI cited "different safeguards."

DeepSeek V4‑Pro: The Price Disruptor

DeepSeek released V4‑Pro and V4‑Flash on April 24 — both MIT‑licensed with weights on Hugging Face. DeepSeek researcher Deli Chen commented on the release on X with the words: > "AGI belongs to everyone." (cited via VentureBeat).

Architecture: V4‑Pro is a 1.6T‑parameter MoE with 49B active parameters. The key innovation is hybrid attention (Compressed Sparse Attention + Heavily Compressed Attention), which according to the DeepSeek tech report at 1M‑token context only requires 27% of inference FLOPs and 10% of KV cache compared to V3.2.

Strengths:

• Codeforces rating 3,206 — higher than GPT‑5.4 (3,168) and Gemini 3.1 Pro (3,052).
• LiveCodeBench 93.5% — leads the field, ahead of Gemini (91.7%) and Claude (88.8%).
• SWE‑bench Verified 80.6% — 0.2 points behind Claude Opus 4.6.
• Price: $0.145/M input and $1.74/M output — 7× cheaper input and 6× cheaper output than GPT‑5.5 or Opus 4.7. At 100M output tokens monthly, that's $174 vs. $2,500.
• Open weights, MIT license.

Weaknesses:

• SWE‑bench Pro 55.4% — behind Opus 4.7 (64.3%) and GPT‑5.5 (58.6%).
• Humanity's Last Exam without tools 37.7% — versus 41.4% (GPT‑5.5) and 46.9% (Opus 4.7).
• SimpleQA‑Verified 57.9% vs. Gemini 75.6%.
• Preview status: both V4 models are explicitly preview.

Kimi K2.6: The Open‑Weight Specialist for Coding Agents

Moonshot AI released Kimi K2.6 on April 20 — a 1T‑parameter MoE with 32B active parameters, 256K context, Modified MIT License. The differentiator: K2.6 was explicitly trained for long agentic coding sessions and ships with an Agent Swarm orchestrator that coordinates up to 300 parallel sub‑agents. Scott Breitenother (CEO Kilo Code) is quoted in the official announcement:

"K2.6 offers SOTA-level performance at a fraction of the cost. It's tremendously good at long-context tasks across the codebase, as well as the day-to-day work needed to support an always-on agent like KiloClaw."

Strengths:

• SWE‑bench Pro 58.6% — tied with GPT‑5.5, beats GPT‑5.4 (57.7%) and GLM‑5.1 (58.4%) by a hair.
• SWE‑bench Verified 80.2%.
• Price: $0.60–$0.95 / $4.00 per M tokens. With cache, 25× cheaper than Opus 4.7.
• Long‑horizon stability: 13 hours of continuous autonomous coding demonstrated.

Weaknesses:

• Pure reasoning / math: GPT‑5.4 leads AIME 2026 (99.2% vs. K2.6 96.4%) and GPQA Diamond (92.8% vs. 90.5%).
• Tool‑call reliability: CodeRouter puts it this way: > "The gap is narrowing — K2.6 is visibly better than K2.5 — but for apps that absolutely require structured-output reliability, Anthropic is still the floor."
• Context window: 256K vs. 1M for GPT‑5.5, Opus 4.7, and V4‑Pro.

GLM‑5.1: The Huawei Ascend Story

Z.ai (formerly Zhipu AI) released GLM‑5.1 on April 7. The noteworthy point is not the benchmark result, but that the model was trained entirely on a 100,000‑chip cluster of Huawei Ascend 910B. Zero Nvidia GPUs. China can now train frontier models without US hardware.

Strengths: SWE‑bench Pro 58.4%, MIT license, freely self‑hostable, deep reasoning on mathematical tasks.

Weaknesses: Overtaken by Kimi K2.6 two weeks later; ecosystem maturity lags Anthropic and OpenAI.

SWE‑bench Pro: Direct Comparison

What's striking is not Opus 4.7's top score, but how tightly packed the middle is: GPT‑5.5, Kimi K2.6, and GLM‑5.1 differ by 0.2 percentage points. At that spread, price, license, and tooling maturity matter more than the benchmark number.

Extended Benchmark Comparison

(Sources: vendor tables, VentureBeat, llm‑stats.com.)

Which Model for What?

Production coding agent, quality first — Claude Opus 4.7. The lead on SWE‑bench Pro, the literal instruction following, and the vision improvements justify the premium price for tasks where errors are expensive.

Terminal/DevOps/browser automation — GPT‑5.5. Terminal‑Bench 2.0 at 82.7% and BrowseComp at 84.4% are clearly the top.

High‑volume processing with good coding quality — DeepSeek V4‑Pro or Kimi K2.6.

Data sovereignty — self‑host DeepSeek V4, Kimi K2.6, or GLM‑5.1.

Cheapest practical model for standard tasks — Sonnet 4.6 or DeepSeek V4‑Flash.

What Doesn't Make It Into the Marketing

1. Benchmark inflation is real. Self‑reported figures don't fully match real‑world behavior.
2. Tokenizer changes obscure pricing moves. List prices and effective costs are not the same.
3. "Open weights" is not "open source". Training code and data are not fully open.

Conclusion

The frontier field has narrowed, not widened. Anthropic, OpenAI, Moonshot, DeepSeek, and Z.ai are shipping models within the same weeks, sitting within a few percentage points on most benchmarks. What actually decides is the surrounding constraints: economics per million tokens, data privacy requirements, tool‑call reliability, and ecosystem maturity.

Anyone still believing one model can solve every task optimally hasn't been paying attention to the last three weeks.

Sources

Vendor primary sources

• OpenAI: Introducing GPT‑5.5, GPT‑5.5 System Card
• Anthropic Opus 4.7 profile: llm‑stats.com
• DeepSeek V4: api‑docs.deepseek.com, weights on Hugging Face
• Moonshot Kimi K2.6: Cloudflare Workers AI changelog

Independent analyses

• Vellum: Claude Opus 4.7 Benchmarks Explained
• VentureBeat: DeepSeek‑V4 at 1/6th the cost
• CodeRouter: Kimi K2.6 Review
• TechCrunch: OpenAI releases GPT‑5.5
• Finout: Claude Opus 4.7 Pricing Reality
• Artificial Analysis Index v4.0: artificialanalysis.ai