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.
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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
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.
200K cached + 20K fresh input + 10K output tokens
Interfaze Beta
Interfaze Beta has the lower estimated token cost for this stated workload. Claude Opus 4.6 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.
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.
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.
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.
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
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.
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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 6.2GPQAKnowledge
Normalized gap 1.4Each 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.
| Category | Claude Opus 4.6 | Interfaze Beta | Basis | Reading |
|---|---|---|---|---|
| Agentic | 44.3Supported · #39/105 | Not ranked | Not comparableBenchAlign v5.7 lane · 10 vs 0 public rows | Not comparable |
| Coding | 51.1Supported · #36/135 | Not ranked | Not comparableBenchAlign v5.7 lane · 8 vs 1 public rows | Not comparable |
| Reasoning | 68.1Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 59.6#30/50 | 27.6Unranked · 5 rankable rows | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Knowledge | 58.7Estimated · #37/158 | Not ranked | Not comparableBenchAlign v5.7 lane · 9 vs 3 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 51.0#79/124 | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 58.5Unranked · 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 v5.7) 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.
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.
1K fresh input + 500 output tokens
Interfaze Beta has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Interfaze Beta has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Interfaze Beta has the lower modeled cost
Claude Opus 4.6 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.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Opus 4.6
1M
Interfaze Beta
1M
Claude Opus 4.6
Not sourced
Interfaze Beta
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.6
Not published
Interfaze Beta
Not published
Claude Opus 4.6
Not sourced
Interfaze Beta
Not sourced
Claude Opus 4.6
Not sourced
Interfaze Beta
Not sourced
Claude Opus 4.6
Not sourced
Interfaze Beta
Not sourced
Claude Opus 4.6
Non-Reasoning
Interfaze Beta
Reasoning
Claude Opus 4.6
Proprietary
Interfaze Beta
Proprietary
Claude Opus 4.6
Proprietary
Interfaze Beta
Proprietary
Claude Opus 4.6
2026-02-01
Interfaze Beta
2026-05-11
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.
Interfaze Beta is not ranked on the public lane for coding, so no winner is named for coding.
Interfaze Beta is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
For the stated presets, chat costs $0.0175 on Claude Opus 4.6 and $0.00325 on Interfaze Beta; repository review costs $0.325 and $0.0855; the cache-heavy agent loop costs $1.35 and $0.365. Claude Opus 4.6 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.
Both models list the same context window, 1M.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
BrowseComp
Not directly comparable
OSWorld-Verified
Not directly comparable
Claw-Eval
Not directly comparable
DeepSearchQA
Not directly comparable
CyberGym
Not directly comparable
Gert Labs
Not directly comparable
ResearchClawBench
Not directly comparable
JobBench
Not directly comparable
ApprenticeBench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench Verified*
Not directly comparable
LiveCodeBench Pro
Not directly comparable
SWE-bench Pro
Not directly comparable
SWE-Rebench
Not directly comparable
React Native Evals
Not directly comparable
Vibe Code Bench
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
Spider 2.0-Lite
Not directly comparable
MMMU-Pro
Claude Opus 4.6 leads this result
ERQA
Not directly comparable
ScreenSpot Pro
Not directly comparable
MedXpertQA (MM)
Not directly comparable
OCRBench V2
Not directly comparable
olmOCR
Not directly comparable
RefCOCO (avg)
Not directly comparable
GPQA
Claude Opus 4.6 leads this result
GPQA-D
Interfaze Beta leads this result
SuperGPQA
Not directly comparable
MMLU-Pro
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
HLE
Not directly comparable
HLE w/o tools
Not directly comparable
HealthBench Hard
Not directly comparable
MedXpertQA (Text)
Not directly comparable
MMMLU
Not directly comparable
SOB Value Acc
Not directly comparable
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Last updated September 24, 2026