Agentic
Directional only- GPT-5.5
- 63.9
- Supported · #15/151
- GPT-OSS 120B
- 44.9
- Estimated · #97/151
- Basis
- BenchAlign lane · 13 vs 1 public rows
- Reading
- Directional only
GPT-5.5 vs GPT-OSS 120B
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
GPT-5.5 has the higher public score, 73.27 versus 49.26, and the 90% score intervals do not overlap.
2 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
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
GPT-OSS 120B 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
GPT-OSS 120B is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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. GPT-OSS 120B does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
36 GPT-5.5 only
2 Shared
- Shared results
- 2
- GPT-5.5 only
- 36
- GPT-OSS 120B only
- 0
- Like-for-like categories
- 0 / 8
4 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.
Coding
Directional only- GPT-5.5
- 67.7
- Supported · #8/183
- GPT-OSS 120B
- 46.2
- Estimated · #100/183
- Basis
- BenchAlign lane · 9 vs 1 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.5
- 73.3
- Supported · #7/181
- GPT-OSS 120B
- 44.4
- Estimated · #118/181
- Basis
- BenchAlign lane · 6 vs 0 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.5
- 92.9
- #7/120
- GPT-OSS 120B
- 84.0
- #46/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- GPT-5.5
- 63.5
- #15/22
- GPT-OSS 120B
- 57.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.5
- 69.6
- Unranked · 3 rankable rows
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.5
- Not ranked
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.5
- 71.3
- #19/48
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.5 | GPT-OSS 120B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 63.9Supported · #15/151 | 44.9Estimated · #97/151 | Directional onlyBenchAlign lane · 13 vs 1 public rows | Directional only |
| Coding | 67.7Supported · #8/183 | 46.2Estimated · #100/183 | Directional onlyBenchAlign lane · 9 vs 1 public rows | Directional only |
| Knowledge | 73.3Supported · #7/181 | 44.4Estimated · #118/181 | Directional onlyBenchAlign lane · 6 vs 0 public rows | Directional only |
| Instruction following | 92.9#7/120 | 84.0#46/120 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 63.5#15/22 | 57.7Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 69.6Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 71.3#19/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 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
- GPT-5.5
- $0.02
- Fits in one request
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Fits in one request
GPT-OSS 120B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-5.5
- $0.34
- Fits in one request
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Fits in one request
GPT-OSS 120B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.5
- $0.5
- Fits in one request
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
GPT-OSS 120B does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.5GPT-OSS 120B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.5
GPT-OSS 120B
128K
API model ID
GPT-5.5
gpt-5.5
OpenAI pricingGPT-OSS 120B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.5
$0.5 per 1M cached input tokens
OpenAI pricingGPT-OSS 120B
No comparable hosted API rate
Documented inputs
GPT-5.5
Not sourced
GPT-OSS 120B
Not sourced
Documented outputs
GPT-5.5
Not sourced
GPT-OSS 120B
Not sourced
Provider availability
GPT-5.5
Not sourced
GPT-OSS 120B
Not sourced
Reasoning profile
GPT-5.5
Reasoning
GPT-OSS 120B
Non-Reasoning
Weight access
GPT-5.5
Proprietary
GPT-OSS 120B
Open Weight
License
GPT-5.5
Proprietary
GPT-OSS 120B
Open Weight
Release date
GPT-5.5
2026-04-23
GPT-OSS 120B
2025-08-05
If you already use one of these models
- Deployment change
- Both entries list OpenAI as the provider. Confirm endpoint, model ID, limits, and feature support before switching.
- Quality signal
- GPT-5.5 has the higher public score, 73.27 versus 49.26, and the 90% score intervals do not overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- 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
GPT-5.582%SourceGPT-OSS 120B—Not directly comparable
CyberGym
GPT-5.581.8%SourceGPT-OSS 120B—Not directly comparable
BrowseComp
GPT-5.584.4%SourceGPT-OSS 120B—Not directly comparable
OSWorld-Verified
GPT-5.578.7%SourceGPT-OSS 120B—Not directly comparable
MCP Atlas
GPT-5.575.3%SourceGPT-OSS 120B—Not directly comparable
Toolathlon
GPT-5.555.6%SourceGPT-OSS 120B—Not directly comparable
τ²-bench results
GPT-5.598%SourceGPT-OSS 120B—Not directly comparable
Gert Labs
Shared sourceGPT-5.572.93%GPT-OSS 120B29.61%GPT-5.5 leads this result
ResearchClawBench
GPT-5.517.0%SourceGPT-OSS 120B—Not directly comparable
OSWorld 2.0
GPT-5.513.0%SourceGPT-OSS 120B—Not directly comparable
JobBench
GPT-5.542.7%SourceGPT-OSS 120B—Not directly comparable
ExploitGym
GPT-5.513.4%SourceGPT-OSS 120B—Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.576.4%SourceGPT-OSS 120B—Not directly comparable
Coding
SWE-bench Pro
GPT-5.558.6%SourceGPT-OSS 120B—Not directly comparable
Terminal-Bench 2.0
GPT-5.582.0%SourceGPT-OSS 120B—Not directly comparable
Vibe Code Bench
GPT-5.569.85%SourceGPT-OSS 120B—Not directly comparable
React Native Evals
Shared sourceGPT-5.584.7%GPT-OSS 120B71.6%GPT-5.5 leads this result
cursorBench31
GPT-5.559.2%SourceGPT-OSS 120B—Not directly comparable
cursorBench32
GPT-5.558.4%SourceGPT-OSS 120B—Not directly comparable
FrontierCode 1.1 Main
GPT-5.543.0%SourceGPT-OSS 120B—Not directly comparable
LiveCodeBench (Vals)
GPT-5.585.3%SourceGPT-OSS 120B—Not directly comparable
SWE-bench (Vals)
GPT-5.582.6%SourceGPT-OSS 120B—Not directly comparable
Reasoning
Knowledge
GPQA
GPT-5.593.6%SourceGPT-OSS 120B—Not directly comparable
GPQA-D
GPT-5.593.6%SourceGPT-OSS 120B—Not directly comparable
HLE
GPT-5.552.2%SourceGPT-OSS 120B—Not directly comparable
HLE w/o tools
GPT-5.541.4%SourceGPT-OSS 120B—Not directly comparable
GPQA Diamond (Vals)
GPT-5.593.2%SourceGPT-OSS 120B—Not directly comparable
MMLU-Pro (Vals)
GPT-5.588.1%SourceGPT-OSS 120B—Not directly comparable
Math
Multimodal
Frequently asked questions
Which is better, GPT-5.5 or GPT-OSS 120B?
GPT-5.5 has the higher public score, 73.27 versus 49.26, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, GPT-5.5 or GPT-OSS 120B?
GPT-5.5 scores higher for coding on the public lane, 67.7 to 46.2. GPT-OSS 120B 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, GPT-5.5 or GPT-OSS 120B?
GPT-5.5 scores higher for agentic tasks on the public lane, 63.9 to 44.9. GPT-OSS 120B is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.
Which costs less, GPT-5.5 or GPT-OSS 120B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, GPT-5.5 or GPT-OSS 120B?
GPT-5.5 has the larger documented context window: 1M, compared with 128K.
Related comparisons
Last updated September 4, 2026
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