Long documents
Prompts that approach the documented context limit
Gemini 2.5 Pro
Gemini 2.5 Pro has the larger documented context window.
Confidence: documented
Gemini 2.5 Pro vs GPT-OSS 120B
Updated October 17, 2026. Rank says Gemini 2.5 Pro is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
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Decision reading
Gemini 2.5 Pro has the higher public score estimate, 49.14 versus 37.32, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 5 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 5
- Gemini 2.5 Pro only
- 4
- GPT-OSS 120B only
- 3
- Like-for-like categories
- 1 / 8
Supported: Gemini 2.5 Pro 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
Gemini 2.5 Pro and GPT-OSS 120B are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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-OSS 120B has no comparable published API token rate.
Confidence: listed-ratesRepository 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.
22.3Gemini 2.5 Pro23.0GPT-OSS 120B
Directional only · BenchAlign v5.8
GPT-OSS 120B 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.
4 Gemini 2.5 Pro only
5 Shared
3 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.
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.
HLEKnowledge
Normalized gap 3.9Gemini 2.5 Pro:18.8%GPT-OSS 120B:14.9%GPQAKnowledge
Normalized gap 2.9Gemini 2.5 Pro:83%GPT-OSS 120B:80.1%SWE-bench VerifiedCoding
Normalized gap 1.4Gemini 2.5 Pro:63.8%GPT-OSS 120B:62.4%
Bars run 0–100 on each benchmark’s normalized display scale
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.
Knowledge
Like-for-like- Gemini 2.5 Pro
- 45.6
- Supported · #96/184
- GPT-OSS 120B
- 38.6
- Supported · #122/184
- Basis
- BenchAlign v5.8 lane · 2 vs 3 public rows
- Reading
- Gemini 2.5 Pro leads · intervals overlap
Agentic
Directional only- Gemini 2.5 Pro
- 25.7
- Estimated · #102/128
- GPT-OSS 120B
- 19.5
- Estimated · #110/128
- Basis
- BenchAlign v5.8 lane · 1 vs 1 public rows
- Reading
- Directional only
Coding
Directional only- Gemini 2.5 Pro
- 22.3
- Supported · #115/150
- GPT-OSS 120B
- 23.0
- Estimated · #111/150
- Basis
- BenchAlign v5.8 lane · 3 vs 2 public rows
- Reading
- Directional only
Instruction following
Directional only- Gemini 2.5 Pro
- 57.8
- #83/148
- GPT-OSS 120B
- 84.3
- #50/148
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 2.5 Pro
- 73.3
- Unranked · 2 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- Gemini 2.5 Pro
- 74.8
- Unranked · 1 rankable row
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 2.5 Pro
- Not ranked
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 2.5 Pro
- 36.9
- Unranked · 3 rankable rows
- GPT-OSS 120B
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 2.5 Pro | GPT-OSS 120B | Basis | Reading |
|---|---|---|---|---|
| Knowledge | 45.6Supported · #96/184 | 38.6Supported · #122/184 | Like-for-likeBenchAlign v5.8 lane · 2 vs 3 public rows | Gemini 2.5 Pro leads · intervals overlap |
| Agentic | 25.7Estimated · #102/128 | 19.5Estimated · #110/128 | Directional onlyBenchAlign v5.8 lane · 1 vs 1 public rows | Directional only |
| Coding | 22.3Supported · #115/150 | 23.0Estimated · #111/150 | Directional onlyBenchAlign v5.8 lane · 3 vs 2 public rows | Directional only |
| Instruction following | 57.8#83/148 | 84.3#50/148 | Directional onlyProvisional lane · 0 vs 0 weighted rows | Directional only |
| Reasoning | 73.3Unranked · 2 rankable rows | 61.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 74.8Unranked · 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 | 36.9Unranked · 3 rankable rows | 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 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
- Gemini 2.5 Pro
- $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
- Gemini 2.5 Pro
- $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
- Gemini 2.5 Pro
- $0.15
- 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.
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.
SpecificationGemini 2.5 ProGPT-OSS 120B
Context window
Maximum documented context; output-token limits may be lower.
Gemini 2.5 Pro
GPT-OSS 120B
128K
API model ID
Gemini 2.5 Pro
gemini-2.5-pro
Google Gemini API 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.
Gemini 2.5 Pro
$0.125 per 1M cached input tokens
Google Gemini API pricingGPT-OSS 120B
No comparable hosted API rate
Documented inputs
Gemini 2.5 Pro
Not sourced
GPT-OSS 120B
Not sourced
Documented outputs
Gemini 2.5 Pro
Not sourced
GPT-OSS 120B
Not sourced
Provider availability
Gemini 2.5 Pro
Not sourced
GPT-OSS 120B
Not sourced
Reasoning profile
Gemini 2.5 Pro
Non-Reasoning
GPT-OSS 120B
Non-Reasoning
Weight access
Gemini 2.5 Pro
Proprietary
GPT-OSS 120B
Open Weight
License
Gemini 2.5 Pro
Proprietary
GPT-OSS 120B
Open Weight
Release date
Gemini 2.5 Pro
2025-03-01
GPT-OSS 120B
2025-08-05
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
- Gemini 2.5 Pro has the higher public score estimate, 49.14 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
- Gemini 2.5 Pro has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Gemini 2.5 Pro or GPT-OSS 120B?
Gemini 2.5 Pro has the higher public score estimate, 49.14 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, Gemini 2.5 Pro or GPT-OSS 120B?
GPT-OSS 120B scores higher for coding on the public lane, 23 to 22.3. 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, Gemini 2.5 Pro or GPT-OSS 120B?
Gemini 2.5 Pro scores higher for agentic tasks on the public lane, 25.7 to 19.5. Gemini 2.5 Pro and GPT-OSS 120B are 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, Gemini 2.5 Pro 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, Gemini 2.5 Pro or GPT-OSS 120B?
Gemini 2.5 Pro has the larger documented context window: 1M, compared with 128K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Gemini 2.5 Pro
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 evidence12 rows
Agentic
Gert Labs
Shared sourceGemini 2.5 Pro42.01%GPT-OSS 120B29.61%Gemini 2.5 Pro leads this result
Coding
SWE-bench Verified
Gemini 2.5 Pro63.8%SourceGPT-OSS 120B62.4%SourceGemini 2.5 Pro leads this result
Vibe Code Bench
Gemini 2.5 Pro0.40%SourceGPT-OSS 120B—Not directly comparable
SWE-bench (Vals)
Gemini 2.5 Pro54.4%SourceGPT-OSS 120B—Not directly comparable
React Native Evals
Gemini 2.5 Pro—GPT-OSS 120B71.6%SourceNot directly comparable
Knowledge
Math
AIME 2024
Gemini 2.5 Pro92%SourceGPT-OSS 120B95.8%SourceGPT-OSS 120B leads this result
FrontierMath v2 (Tiers 1-3)
Gemini 2.5 Pro24.561%SourceGPT-OSS 120B—Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 2.5 Pro0.000%SourceGPT-OSS 120B—Not directly comparable
AIME 2025
Gemini 2.5 Pro—GPT-OSS 120B92.5%SourceNot directly comparable
12 public results · 5 shared
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Last updated October 17, 2026