Coding
Directional only- Claude 3 Haiku
- 22.5
- Estimated · #181/183
- GPT-5.5
- 67.7
- Supported · #8/183
- Basis
- BenchAlign lane · 0 vs 9 public rows
- Reading
- Directional only
Claude 3 Haiku vs GPT-5.5
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.
0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Long documents
Prompts that approach the documented context limit
GPT-5.5
GPT-5.5 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Claude 3 Haiku
Claude 3 Haiku has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Claude 3 Haiku
Claude 3 Haiku 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
Claude 3 Haiku is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude 3 Haiku is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. Claude 3 Haiku does not fit this workload in one request. Claude 3 Haiku has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
38 GPT-5.5 only
- Shared results
- 0
- Claude 3 Haiku only
- 0
- GPT-5.5 only
- 38
- Like-for-like categories
- 0 / 8
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.
Knowledge
Directional only- Claude 3 Haiku
- 24.9
- Estimated · #178/181
- GPT-5.5
- 73.3
- Supported · #7/181
- Basis
- BenchAlign lane · 0 vs 6 public rows
- Reading
- Directional only
Instruction following
Directional only- Claude 3 Haiku
- 41.3
- #101/120
- GPT-5.5
- 92.9
- #7/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- Claude 3 Haiku
- Not ranked
- GPT-5.5
- 63.9
- Supported · #15/151
- Basis
- BenchAlign lane · 0 vs 13 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude 3 Haiku
- 39.0
- Unranked · 2 rankable rows
- GPT-5.5
- 63.5
- #15/22
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude 3 Haiku
- Not ranked
- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude 3 Haiku
- Not ranked
- GPT-5.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude 3 Haiku
- 25.3
- Unranked · 1 rankable row
- GPT-5.5
- 71.3
- #19/48
- Basis
- Provisional lane · 0 vs 2 weighted rows
- Reading
- Not comparable
| Category | Claude 3 Haiku | GPT-5.5 | Basis | Reading |
|---|---|---|---|---|
| Coding | 22.5Estimated · #181/183 | 67.7Supported · #8/183 | Directional onlyBenchAlign lane · 0 vs 9 public rows | Directional only |
| Knowledge | 24.9Estimated · #178/181 | 73.3Supported · #7/181 | Directional onlyBenchAlign lane · 0 vs 6 public rows | Directional only |
| Instruction following | 41.3#101/120 | 92.9#7/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 63.9Supported · #15/151 | Not comparableBenchAlign lane · 0 vs 13 public rows | Not comparable |
| Reasoning | 39.0Unranked · 2 rankable rows | 63.5#15/22 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Math | Not ranked | 69.6Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 25.3Unranked · 1 rankable row | 71.3#19/48 | Not comparableProvisional lane · 0 vs 2 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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 3 Haiku
- $0.00088
- Fits in one request
- GPT-5.5
- $0.02
- Fits in one request
Claude 3 Haiku has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude 3 Haiku
- $0.01625
- Fits in one request
- GPT-5.5
- $0.34
- Fits in one request
Claude 3 Haiku 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 3 Haiku
- $0.0675
- Does not fit in one request
- Cached input priced at the published list-input rate
- GPT-5.5
- $0.5
- Fits in one request
Claude 3 Haiku does not fit this workload in one request. Claude 3 Haiku 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 3 HaikuGPT-5.5
Context window
Maximum documented context; output-token limits may be lower.
Claude 3 Haiku
200K
GPT-5.5
API model ID
Claude 3 Haiku
Not sourced
GPT-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 3 Haiku
Not published
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingDocumented inputs
Claude 3 Haiku
Not sourced
GPT-5.5
Not sourced
Documented outputs
Claude 3 Haiku
Not sourced
GPT-5.5
Not sourced
Provider availability
Claude 3 Haiku
Not sourced
GPT-5.5
Not sourced
Reasoning profile
Claude 3 Haiku
Non-Reasoning
GPT-5.5
Reasoning
Weight access
Claude 3 Haiku
Proprietary
GPT-5.5
Proprietary
License
Claude 3 Haiku
Proprietary
GPT-5.5
Proprietary
Release date
Claude 3 Haiku
2024-03-01
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.01625 vs $0.34. Cache-heavy agent loop: $0.0675 vs $0.5.
- Context tradeoff
- GPT-5.5 has the larger documented window (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 evidence38 rows
Agentic
Terminal-Bench 2.0
Claude 3 Haiku—GPT-5.582%SourceNot directly comparable
CyberGym
Claude 3 Haiku—GPT-5.581.8%SourceNot directly comparable
BrowseComp
Claude 3 Haiku—GPT-5.584.4%SourceNot directly comparable
OSWorld-Verified
Claude 3 Haiku—GPT-5.578.7%SourceNot directly comparable
MCP Atlas
Claude 3 Haiku—GPT-5.575.3%SourceNot directly comparable
Toolathlon
Claude 3 Haiku—GPT-5.555.6%SourceNot directly comparable
τ²-bench results
Claude 3 Haiku—GPT-5.598%SourceNot directly comparable
Gert Labs
Claude 3 Haiku—GPT-5.572.93%SourceNot directly comparable
ResearchClawBench
Claude 3 Haiku—GPT-5.517.0%SourceNot directly comparable
OSWorld 2.0
Claude 3 Haiku—GPT-5.513.0%SourceNot directly comparable
JobBench
Claude 3 Haiku—GPT-5.542.7%SourceNot directly comparable
ExploitGym
Claude 3 Haiku—GPT-5.513.4%SourceNot directly comparable
Terminal-Bench 2.1 (Vals)
Claude 3 Haiku—GPT-5.576.4%SourceNot directly comparable
Coding
SWE-bench Pro
Claude 3 Haiku—GPT-5.558.6%SourceNot directly comparable
Terminal-Bench 2.0
Claude 3 Haiku—GPT-5.582.0%SourceNot directly comparable
Vibe Code Bench
Claude 3 Haiku—GPT-5.569.85%SourceNot directly comparable
React Native Evals
Claude 3 Haiku—GPT-5.584.7%SourceNot directly comparable
cursorBench31
Claude 3 Haiku—GPT-5.559.2%SourceNot directly comparable
cursorBench32
Claude 3 Haiku—GPT-5.558.4%SourceNot directly comparable
FrontierCode 1.1 Main
Claude 3 Haiku—GPT-5.543.0%SourceNot directly comparable
LiveCodeBench (Vals)
Claude 3 Haiku—GPT-5.585.3%SourceNot directly comparable
SWE-bench (Vals)
Claude 3 Haiku—GPT-5.582.6%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Claude 3 Haiku—GPT-5.593.6%SourceNot directly comparable
GPQA-D
Claude 3 Haiku—GPT-5.593.6%SourceNot directly comparable
HLE
Claude 3 Haiku—GPT-5.552.2%SourceNot directly comparable
HLE w/o tools
Claude 3 Haiku—GPT-5.541.4%SourceNot directly comparable
GPQA Diamond (Vals)
Claude 3 Haiku—GPT-5.593.2%SourceNot directly comparable
MMLU-Pro (Vals)
Claude 3 Haiku—GPT-5.588.1%SourceNot directly comparable
Math
Multimodal
Frequently asked questions
Which is better, Claude 3 Haiku or GPT-5.5?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Claude 3 Haiku or GPT-5.5?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 22.5. Claude 3 Haiku is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which is better for agentic tasks, Claude 3 Haiku or GPT-5.5?
Claude 3 Haiku is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude 3 Haiku or GPT-5.5?
For the stated presets, chat costs $0.00088 on Claude 3 Haiku and $0.02 on GPT-5.5; repository review costs $0.01625 and $0.34; the cache-heavy agent loop costs $0.0675 and $0.5. Claude 3 Haiku does not fit this workload in one request. Claude 3 Haiku has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude 3 Haiku or GPT-5.5?
GPT-5.5 has the larger documented context window: 1M, compared with 200K.
Related comparisons
Last updated September 4, 2026
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