Long documents
Prompts that approach the documented context limit
Interfaze Beta
Interfaze Beta has the larger documented context window.
Confidence: documented
GPT-5.2 vs Interfaze Beta
Updated September 24, 2026. We do not rank this pair: at least one has no public score. Public scores include evidence status and uncertainty.
Both of these models will change. Get the price, version and retirement notices for the pair, each with its source. Follow model changes
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 2
- GPT-5.2 only
- 13
- Interfaze Beta only
- 7
- Like-for-like categories
- 0 / 8
Supported: GPT-5.2How 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.
Chat turn cost
1K fresh input + 500 output tokens
Interfaze Beta
Interfaze Beta has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Interfaze Beta
Interfaze Beta has the lower estimated token cost for this stated workload. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Interfaze Beta 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
Interfaze Beta
Interfaze Beta has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Interfaze Beta is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
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.
41.0GPT-5.2—Interfaze Beta
Not comparable · BenchAlign v5.7
The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.
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.
13 GPT-5.2 only
2 Shared
7 Interfaze Beta only
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.
MMMU-ProMultimodal
Normalized gap 8.4GPT-5.2:79.5%Interfaze Beta:71.1%GPQAKnowledge
Normalized gap 2.5GPT-5.2:92.4%Interfaze Beta:89.9%
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 v5.7 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.
Agentic
Not comparable- GPT-5.2
- 42.6
- Supported · #42/105
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 4 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-5.2
- 41.0
- Supported · #54/135
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 3 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-5.2
- 60.6
- Unranked · 3 rankable rows
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-5.2
- 66.3
- #23/50
- Interfaze Beta
- 27.6
- Unranked · 5 rankable rows
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-5.2
- 57.9
- Supported · #38/158
- Interfaze Beta
- Not ranked
- Basis
- BenchAlign v5.7 lane · 1 vs 3 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-5.2
- Not ranked
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-5.2
- 91.2
- #15/124
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-5.2
- 57.4
- Unranked · 2 rankable rows
- Interfaze Beta
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-5.2 | Interfaze Beta | Basis | Reading |
|---|---|---|---|---|
| Agentic | 42.6Supported · #42/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 4 vs 0 public rows | Not comparable |
| Coding | 41.0Supported · #54/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 3 vs 1 public rows | Not comparable |
| Reasoning | 60.6Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 66.3#23/50 | 27.6Unranked · 5 rankable rows | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Knowledge | 57.9Supported · #38/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 1 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 91.2#15/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 57.4Unranked · 2 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 v5.7) 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-5.2
- $0.00875
- Fits in one request
- Interfaze Beta
- $0.00325
- Fits in one request
Interfaze Beta has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- GPT-5.2
- $0.1295
- Fits in one request
- Interfaze Beta
- $0.0855
- Fits in one request
Interfaze Beta has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-5.2
- $0.525
- Fits in one request
- Cached input priced at the published list-input rate
- Interfaze Beta
- $0.365
- Fits in one request
- Cached input priced at the published list-input rate
Interfaze Beta has the lower modeled cost
GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input 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-5.2Interfaze Beta
Context window
Maximum documented context; output-token limits may be lower.
GPT-5.2
400K
Interfaze Beta
1M
API model ID
GPT-5.2
Not sourced
Interfaze Beta
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.2
Not published
Interfaze Beta
Not published
Documented inputs
GPT-5.2
Not sourced
Interfaze Beta
Not sourced
Documented outputs
GPT-5.2
Not sourced
Interfaze Beta
Not sourced
Provider availability
GPT-5.2
Not sourced
Interfaze Beta
Not sourced
Reasoning profile
GPT-5.2
Reasoning
Interfaze Beta
Reasoning
Weight access
GPT-5.2
Proprietary
Interfaze Beta
Proprietary
License
GPT-5.2
Proprietary
Interfaze Beta
Proprietary
Release date
GPT-5.2
2025-12-11
Interfaze Beta
2026-05-11
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- Repository review: $0.1295 vs $0.0855. Cache-heavy agent loop: $0.525 vs $0.365.
- Context tradeoff
- Interfaze Beta has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-5.2 or Interfaze Beta?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-5.2 or Interfaze Beta?
Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-5.2 or Interfaze Beta?
Interfaze Beta is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-5.2 or Interfaze Beta?
For the stated presets, chat costs $0.00875 on GPT-5.2 and $0.00325 on Interfaze Beta; repository review costs $0.1295 and $0.0855; the cache-heavy agent loop costs $0.525 and $0.365. GPT-5.2 has no published cached-input rate, so cached tokens use its listed input rate. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, GPT-5.2 or Interfaze Beta?
Interfaze Beta has the larger documented context window: 1M, compared with 400K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence22 rows
Agentic
Coding
Reasoning
ARC-AGI-2
GPT-5.252.9%SourceInterfaze Beta—Not directly comparable
Multimodal
MMMU-Pro
GPT-5.279.5%SourceInterfaze Beta71.1%SourceGPT-5.2 leads this result
MathVision
GPT-5.283.0%SourceInterfaze Beta—Not directly comparable
CharXiv
GPT-5.282.1%SourceInterfaze Beta—Not directly comparable
V*
GPT-5.275.9%SourceInterfaze Beta—Not directly comparable
OCRBench V2
GPT-5.2—Interfaze Beta70.7%SourceNot directly comparable
olmOCR
GPT-5.2—Interfaze Beta85.7%SourceNot directly comparable
RefCOCO (avg)
GPT-5.2—Interfaze Beta82.1%SourceNot directly comparable
Knowledge
Instruction following
SOB Value Acc
GPT-5.2—Interfaze Beta79.5%SourceNot directly comparable
Math
22 public results · 2 shared
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Last updated September 24, 2026