Long documents
Prompts that approach the documented context limit
GPT-5.1
GPT-5.1 has the larger documented context window.
Confidence: documented
GPT-5.1 vs GPT-OSS 120B
Updated October 17, 2026. Rank says GPT-5.1 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
GPT-5.1 has the higher public score estimate, 57.21 versus 37.32, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 1
- GPT-5.1 only
- 2
- GPT-OSS 120B only
- 7
- Like-for-like categories
- 0 / 8
Supported: GPT-5.1 and GPT-OSS 120BHow the comparison works
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.
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: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
GPT-5.1 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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-5.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 120B has no comparable published API token rate.
Confidence: rate-fallbackRepository 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
Which one for a specific job
Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.
The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.
36.1GPT-5.123.0GPT-OSS 120B
Directional only · BenchAlign v5.8
GPT-5.1 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 GPT-5.1 only
1 Shared
7 GPT-OSS 120B only
3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.8 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-5.1
- 36.1
- Supported · #75/150
- GPT-OSS 120B
- 23.0
- Estimated · #111/150
- Basis
- BenchAlign v5.8 lane · 1 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- GPT-5.1
- 53.3
- Estimated · #67/184
- GPT-OSS 120B
- 38.6
- Supported · #122/184
- Basis
- BenchAlign v5.8 lane · 0 vs 3 public rows
- Reading
- Directional only
Instruction following
Directional only- GPT-5.1
- 89.4
- #30/148
- GPT-OSS 120B
- 84.3
- #50/148
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Agentic
Not comparable- GPT-5.1
- Not ranked
- GPT-OSS 120B
- 19.5
- Estimated · #110/128
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.1
- 80.8
- Unranked · 3 rankable rows
- GPT-OSS 120B
- 61.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.1
- 75.7
- Unranked · 1 rankable row
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.1
- Not ranked
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.1
- Not ranked
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.1 | GPT-OSS 120B | Basis | Reading |
|---|---|---|---|---|
| Coding | 36.1Supported · #75/150 | 23.0Estimated · #111/150 | Directional onlyBenchAlign v5.8 lane · 1 vs 2 public rows | Directional only |
| Knowledge | 53.3Estimated · #67/184 | 38.6Supported · #122/184 | Directional onlyBenchAlign v5.8 lane · 0 vs 3 public rows | Directional only |
| Instruction following | 89.4#30/148 | 84.3#50/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Agentic | Not ranked | 19.5Estimated · #110/128 | Not comparableBenchAlign v5.8 lane · 1 vs 1 public rows | Not comparable |
| Reasoning | 80.8Unranked · 3 rankable rows | 61.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 75.7Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.
Supported evidence per lane · bars run 0–100Methodology
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-5.1
- $0.00625
- 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-5.1
- $0.0925
- 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-5.1
- $0.375
- Fits in one request
- Cached input priced at the published list-input rate
- 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-5.1 has no published cached-input rate, so cached tokens use its listed input rate. GPT-OSS 120B has no comparable published API token rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGPT-5.1GPT-OSS 120B
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.1
400K
GPT-OSS 120B
128K
API model ID
GPT-5.1
Not sourced
GPT-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-5.1
Not published
GPT-OSS 120B
No comparable hosted API rate
Documented inputs
GPT-5.1
Not sourced
GPT-OSS 120B
Not sourced
Documented outputs
GPT-5.1
Not sourced
GPT-OSS 120B
Not sourced
Provider availability
GPT-5.1
Not sourced
GPT-OSS 120B
Not sourced
Reasoning profile
GPT-5.1
Reasoning
GPT-OSS 120B
Non-Reasoning
Weight access
GPT-5.1
Proprietary
GPT-OSS 120B
Open Weight
License
GPT-5.1
Proprietary
GPT-OSS 120B
Open Weight
Release date
GPT-5.1
2025-11-13
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-5.1 has the higher public score estimate, 57.21 versus 37.32, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-5.1 has the larger documented window (400K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.1 or GPT-OSS 120B?
GPT-5.1 has the higher public score estimate, 57.21 versus 37.32, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, GPT-5.1 or GPT-OSS 120B?
GPT-5.1 scores higher for coding on the public lane, 36.1 to 23. 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-5.1 or GPT-OSS 120B?
GPT-5.1 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.1 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-5.1 or GPT-OSS 120B?
GPT-5.1 has the larger documented context window: 400K, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
GPT-5.1
API / mo$8,438
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
GPT-OSS 120B
API / mo$0
Self-host / mo$3,495
Break-even—
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence10 rows
Agentic
Gert Labs
Shared sourceGPT-5.141.24%GPT-OSS 120B29.61%GPT-5.1 leads this result
Coding
Reasoning
ARC-AGI-1
GPT-5.172.83%SourceGPT-OSS 120B—Not directly comparable
Knowledge
Math
10 public results · 1 shared
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Last updated October 17, 2026