Agentic
Like-for-like- Claude Opus 4.8
- 80.3
- GPT-5.5
- 81.6
- Weighted basis
- 3 vs 3 rows
- Reading
- GPT-5.5 leads
Claude Opus 4.8 vs GPT-5.5
Updated August 26, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Opus 4.8 has the higher public score estimate, 76.45 versus 72.95, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
22 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
Which one for your work
Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.
Agentic work
Tool use, computer use, and multi-step task completion
GPT-5.5
GPT-5.5 leads on the same 3 weighted benchmark rows.
Confidence: stronger
Chat turn cost
1K fresh input + 500 output tokens
Claude Opus 4.8
Claude Opus 4.8 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
GPT-5.5
GPT-5.5 has the lower estimated token cost for this stated workload. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Claude Opus 4.8
Claude Opus 4.8 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
11 Claude Opus 4.8 only
22 Shared
11 GPT-5.5 only
- Shared results
- 22
- Claude Opus 4.8 only
- 11
- GPT-5.5 only
- 11
- Like-for-like categories
- 3 / 8
3 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Reasoning
Like-for-like- Claude Opus 4.8
- 72.1
- GPT-5.5
- 85.0
- Weighted basis
- 1 vs 1 rows
- Reading
- GPT-5.5 leads
Knowledge
Like-for-like- Claude Opus 4.8
- 62.7
- GPT-5.5
- 57.8
- Weighted basis
- 2 vs 2 rows
- Reading
- Claude Opus 4.8 leads
Coding
Directional only- Claude Opus 4.8
- 81.1
- GPT-5.5
- 58.6
- Weighted basis
- 2 vs 1 rows
- Reading
- Directional only
Math
Directional only- Claude Opus 4.8
- 53.9
- GPT-5.5
- 47.6
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Multimodal
Directional only- Claude Opus 4.8
- 77.0
- GPT-5.5
- 70.4
- Weighted basis
- 2 vs 2 rows
- Reading
- Directional only
Multilingual
Not comparable- Claude Opus 4.8
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.8
- Not measured
- GPT-5.5
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Opus 4.8 | GPT-5.5 | Weighted basis | Reading |
|---|---|---|---|---|
| Agentic | 80.3 | 81.6 | Like-for-like3 vs 3 rows | GPT-5.5 leads |
| Reasoning | 72.1 | 85.0 | Like-for-like1 vs 1 rows | GPT-5.5 leads |
| Knowledge | 62.7 | 57.8 | Like-for-like2 vs 2 rows | Claude Opus 4.8 leads |
| Coding | 81.1 | 58.6 | Directional only2 vs 1 rows | Directional only |
| Math | 53.9 | 47.6 | Directional only3 vs 2 rows | Directional only |
| Multimodal | 77.0 | 70.4 | Directional only2 vs 2 rows | Directional only |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
Shape of the matched evidence
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.
ARC-AGI-2
Reasoning
OfficeQA Pro
Multimodal
SWE-bench Pro
Coding
Terminal-Bench 2.0
Agentic
HLE
Knowledge
What each workload costs
Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.
Chat turn
1K fresh input + 500 output tokens
- Claude Opus 4.8
- $0.0175
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Claude Opus 4.8 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.8
- $0.325
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Claude Opus 4.8 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.8
- $1.35
- Fits in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
GPT-5.5 has the lower modeled cost
Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.8GPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.8
GPT-5.5
API model ID
Claude Opus 4.8
claude-opus-4-8
Anthropic model overviewGPT-5.5
gpt-5.5
OpenAI pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.8
Not published
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude Opus 4.8
text, image
Anthropic model overviewGPT-5.5
Not sourced
Documented outputs
Claude Opus 4.8
GPT-5.5
Not sourced
Provider availability
Claude Opus 4.8
Generally Available · Claude API
Anthropic model overviewGPT-5.5
Not sourced
Reasoning profile
Claude Opus 4.8
Reasoning
GPT-5.5
Reasoning
Weight access
Claude Opus 4.8
Proprietary
GPT-5.5
Proprietary
License
Claude Opus 4.8
Proprietary
GPT-5.5
Proprietary
Release date
Claude Opus 4.8
2026-05-28
GPT-5.5
2026-04-23
If you already use one of these models
- Deployment change
- The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
- Quality signal
- Claude Opus 4.8 has the higher public score estimate, 76.45 versus 72.95, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.325 vs $0.34. Cache-heavy agent loop: $1.35 vs $0.5.
- Context tradeoff
- Both models list 1M.
Run the same representative tasks against both endpoints before changing production traffic.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence44 rows
Agentic
Terminal-Bench 3.0
Claude Opus 4.821.1%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.874.6%SourceGPT-5.582%SourceGPT-5.5 leads this result
BrowseComp
Claude Opus 4.884.3%SourceGPT-5.584.4%SourceGPT-5.5 leads this result
DeepSearchQA
Claude Opus 4.893.1%SourceGPT-5.5—Not directly comparable
OSWorld-Verified
Claude Opus 4.883.4%SourceGPT-5.578.7%SourceClaude Opus 4.8 leads this result
Finance Agent v2
Claude Opus 4.853.9%SourceGPT-5.5—Not directly comparable
MCP Atlas
Claude Opus 4.882.2%SourceGPT-5.575.3%SourceClaude Opus 4.8 leads this result
Toolathlon
Claude Opus 4.859.9%SourceGPT-5.555.6%SourceClaude Opus 4.8 leads this result
Gert Labs
Shared sourceClaude Opus 4.872.97%GPT-5.572.93%Claude Opus 4.8 leads this result
ResearchClawBench
Shared sourceClaude Opus 4.821.1%GPT-5.517.0%Claude Opus 4.8 leads this result
OSWorld 2.0
Shared sourceClaude Opus 4.820.6%GPT-5.513.0%Claude Opus 4.8 leads this result
CyberGym
Claude Opus 4.8—GPT-5.581.8%SourceNot directly comparable
τ²-bench results
Claude Opus 4.8—GPT-5.598%SourceNot directly comparable
JobBench
Claude Opus 4.8—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Claude Opus 4.8—GPT-5.513.4%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.888.6%SourceGPT-5.5—Not directly comparable
SWE-bench Pro
Claude Opus 4.869.2%SourceGPT-5.558.6%SourceClaude Opus 4.8 leads this result
SWE Multilingual
Claude Opus 4.884.4%SourceGPT-5.5—Not directly comparable
SWE Multimodal
Claude Opus 4.838.4%SourceGPT-5.5—Not directly comparable
Terminal-Bench 2.0
Claude Opus 4.874.6%SourceGPT-5.582.0%SourceGPT-5.5 leads this result
cursorBench31
Shared sourceClaude Opus 4.858.4%GPT-5.559.2%GPT-5.5 leads this result
cursorBench32
Shared sourceClaude Opus 4.862.3%GPT-5.558.4%Claude Opus 4.8 leads this result
FrontierCode 1.1 Main
Shared sourceClaude Opus 4.846.5%GPT-5.543.0%Claude Opus 4.8 leads this result
Vibe Code Bench
Claude Opus 4.8—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Claude Opus 4.8—GPT-5.584.7%SourceNot directly comparable
Reasoning
ARC-AGI-2
Claude Opus 4.872.1%SourceGPT-5.585%SourceGPT-5.5 leads this result
ARC-AGI-3
Shared sourceClaude Opus 4.81.5%GPT-5.50.4%Claude Opus 4.8 leads this result
MRCR v2 64K-128K
Claude Opus 4.8—GPT-5.583.1%SourceNot directly comparable
MRCR v2 128K-256K
Claude Opus 4.8—GPT-5.587.5%SourceNot directly comparable
Knowledge
Math
USAMO 2026
Claude Opus 4.896.7%SourceGPT-5.5—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Shared sourceClaude Opus 4.847.241%GPT-5.551.700%GPT-5.5 leads this result
FrontierMath v2 (Tier 4)
Shared sourceClaude Opus 4.831.250%GPT-5.535.400%GPT-5.5 leads this result
FrontierMath (legacy)
Claude Opus 4.8—GPT-5.551.7%SourceNot directly comparable
Multilingual
INCLUDE
Claude Opus 4.887.6%SourceGPT-5.5—Not directly comparable
Multimodal
OfficeQA Pro
Claude Opus 4.866.2%SourceGPT-5.554.1%SourceClaude Opus 4.8 leads this result
ScreenSpot Pro
Claude Opus 4.887.9%SourceGPT-5.5—Not directly comparable
CharXiv
Claude Opus 4.889.9%SourceGPT-5.5—Not directly comparable
CharXiv w/o tools
Claude Opus 4.880.5%SourceGPT-5.5—Not directly comparable
MMMU-Pro
Claude Opus 4.8—GPT-5.581.2%SourceNot directly comparable
MMMU-Pro w/ Python
Claude Opus 4.8—GPT-5.583.2%SourceNot directly comparable
Frequently asked questions
Which is better, Claude Opus 4.8 or GPT-5.5?
Claude Opus 4.8 has the higher public score estimate, 76.45 versus 72.95, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Opus 4.8 or GPT-5.5?
The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude Opus 4.8 or GPT-5.5?
GPT-5.5 leads the like-for-like agentic tasks comparison across 3 shared weighted benchmark rows.
Which costs less, Claude Opus 4.8 or GPT-5.5?
For the stated presets, chat costs $0.0175 on Claude Opus 4.8 and $0.02 on GPT-5.5; repository review costs $0.325 and $0.34; the cache-heavy agent loop costs $1.35 and $0.5. Claude Opus 4.8 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.8 or GPT-5.5?
Both models list the same context window, 1M.
Related comparisons
Last updated August 26, 2026
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