5-min read · Curated by an AI Systems Architect
Focus: AI Model Benchmarks · Capability Dimensions · Model Selection
In the first part of this series, we saw the overall rankings. But one question remains: There is no "all-round champion."
Claude Opus 4.8 tops BenchLM at 95, but GPT-5.5 is stronger in Agentic capabilities and long context. Choosing a model isn't about picking the highest total score -- it's about picking the one that fits your use case.
This is Part 2 of the series, drilling into 7 capability dimensions -- who is the Top model and Runner-up in each? How big is the gap?
1. Agentic Capability
Top 3: GPT-5.5 (98.0) > Claude Opus 4.8 (97.7) > Gemini 3.5 Flash (96.9)
The difference between the three is only 1.1 points, but their strength profiles are completely different:
| Benchmark | Champion | Score | Runner-up | Score |
|---|---|---|---|---|
| Terminal Tasks | GPT-5.5 | 82.7% | Opus 4.8 | 74.6% |
| Tool Orchestration | Gemini 3.5 Flash | 83.6% | Opus 4.7 | 78.0% |
| Coding Agent | Claude Opus 4.8 | 69.2% (SWE-bench) | GPT-5.5 | 58.6% |
Source: BuildFastWithAI
GPT-5.5's Agentic edge comes from its end-to-end reasoning design -- as a reasoning model, it excels at measurement backtracking and error correction in multi-step agent loops. Meanwhile, Gemini 3.5 Flash, at $1.50/M input, achieves 83.6% on MCP Atlas tool orchestration -- the best Agentic value pick.
2. Coding Capability
Top 3: Claude Opus 4.8 (98.9) > GPT-5.4 / MiniMax M3 (87.2) > GPT-5.5 (84.0)
Opus 4.8 leads by a staggering 11.7 points over the runner-up -- the largest gap across all 7 dimensions.
| Benchmark | Champion | Score | Runner-up | Score |
|---|---|---|---|---|
| SWE-bench Pro | Claude Opus 4.8 | 69.2% | GPT-5.5 | 58.6% |
| LiveCodeBench | DeepSeek V4 Pro | 93.5% (self-reported) | V4 Flash | 91.6% |
Sources: AIMadeTools, DeepSeek V4 Benchmark
Key divergence: Competitive programming and real-world software engineering are two different things. DeepSeek V4 Pro leads LiveCodeBench (competitive programming) at 93.5%, but on SWE-bench Pro (real issue fixing), it's soundly beaten by Opus 4.8. Know your coding scenario before choosing a model.
3. Reasoning & Mathematics
| Benchmark | Champion | Score | Runner-up | Score |
|---|---|---|---|---|
| ARC-AGI-2 (Hardest Reasoning) | GPT-5.5 | 85% | GPT-5.4 Pro | 83.3% |
| HLE (Cross-domain Reasoning) | Claude Opus 4.8 | 57.9% | GPT-5.5 | 53.4% |
| Putnam Math Competition | DeepSeek V4 Pro | 120/120 Perfect | -- | -- |
Sources: BenchLM ARC-AGI-2, AIMadeTools
GPT-5.5 is the only general-purpose model to break the 85% ARC-AGI-2 grand prize threshold -- humans average only 66%. And DeepSeek V4 Pro scores a perfect 120/120 on the Putnam Math Competition, making it the value pick for math scenarios (at one-third the price of GPT-5.5).
4. Knowledge & Multimodal
Knowledge -- Three-way tie: Opus 4.8 (99.3) ≈ GPT-5.4 (99.2) ≈ GPT-5.5 (97.8). The gap is negligible.
Multimodal -- Big divergence:
| Model | Multimodal Score | MMMU-Pro |
|---|---|---|
| Gemini 3.5 Flash | 80.6 | 84.2% |
| Claude Opus 4.8 | 68.8 | -- |
| GPT-5.4 | 60.0 | -- |
| GPT-5.5 | 57.2 | 79.8% |
Source: BuildFastWithAI, BenchLM
GPT-5.5's most significant weakness is multimodal. If your workflow depends on image/video understanding, Gemini 3.5 Flash is the undisputed choice.
5. Long Context
On short text (<100K tokens), the models are close. The real divergence starts at 200K+ tokens:
| Scenario | GPT-5.5 | Claude Opus 4.7 | Gap |
|---|---|---|---|
| 128K Retrieval | 94.8% | 89.1% | 5.7pp |
| 512K-1M Retrieval | 74.0% | 32.2% | 2.3x |
Source: CallSphere
GPT-5.5 is the only reliable choice for long context -- when context exceeds 500K, its retrieval accuracy is more than 2x that of Claude.
6. Capability Dimension Quick Reference
| Dimension | Top Model | Score | Runner-up | Gap |
|---|---|---|---|---|
| Agentic | GPT-5.5 | 98.0 | Claude Opus 4.8 | 0.3 |
| Coding | Claude Opus 4.8 | 98.9 | GPT-5.4 | 11.7 |
| Reasoning | GPT-5.5 | 96.9 | GPT-5.4 | 1.3 |
| Knowledge | Claude Opus 4.8 | 99.3 | GPT-5.4 | 0.1 |
| Multimodal | Gemini 3.5 Flash | 80.6 | Claude Opus 4.8 | 11.8 |
| Long Context | GPT-5.5 | 94.8% | Claude Opus 4.7 | 5.7pp |
| Math Competition | DeepSeek V4 Pro | Perfect 120 | GPT-5.5 | -- |
Next in This Series
Part 3 will reveal two model selection dimensions that 99% of people overlook: Design capability and cost-effectiveness. MiniMax M3 is second only to Claude in design ability? What does a 69x price gap actually mean?
Tomorrow at 7 PM JST.
Sources: BenchLM · BuildFastWithAI · AIMadeTools
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