Agentic
Not comparable- Claude Opus 4.5
- 43.2
- Estimated · #102/154
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 15 vs 1 public rows
- Reading
- Not comparable
Claude Opus 4.5 vs Pareto 26.9
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.
1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Updated September 18, 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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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
Pareto 26.9
Pareto 26.9 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Show secondary and unsupported calls
Repository review cost
50K fresh input + 3K output tokens
Pareto 26.9
Pareto 26.9 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Pareto 26.9 is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
Confidence: limited
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. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
44 Claude Opus 4.5 only
1 Shared
3 Pareto 26.9 only
- Shared results
- 1
- Claude Opus 4.5 only
- 44
- Pareto 26.9 only
- 3
- Like-for-like categories
- 0 / 8
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
Not comparable- Claude Opus 4.5
- 56.7
- Estimated · #36/154
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 5 vs 1 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Opus 4.5
- 72.1
- Unranked · 4 rankable rows
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Opus 4.5
- 54.0
- Estimated · #60/184
- Pareto 26.9
- Not ranked
- Basis
- BenchAlign lane · 6 vs 1 public rows
- Reading
- Not comparable
Math
Not comparable- Claude Opus 4.5
- 57.9
- #6/7
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 4 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Opus 4.5
- 82.9
- #2/12
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Opus 4.5
- 23.5
- #46/48
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 2 vs 1 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Opus 4.5
- 30.6
- #114/124
- Pareto 26.9
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Opus 4.5 | Pareto 26.9 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 43.2Estimated · #102/154 | Not ranked | Not comparableBenchAlign lane · 15 vs 1 public rows | Not comparable |
| Coding | 56.7Estimated · #36/154 | Not ranked | Not comparableBenchAlign lane · 5 vs 1 public rows | Not comparable |
| Reasoning | 72.1Unranked · 4 rankable rows | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Knowledge | 54.0Estimated · #60/184 | Not ranked | Not comparableBenchAlign lane · 6 vs 1 public rows | Not comparable |
| Math | 57.9#6/7 | Not ranked | Not comparableProvisional lane · 4 vs 0 weighted rows | Not comparable |
| Multilingual | 82.9#2/12 | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 23.5#46/48 | Not ranked | Not comparableProvisional lane · 2 vs 1 weighted rows | Not comparable |
| Instruction following | 30.6#114/124 | Not ranked | Not comparableProvisional lane · 1 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.
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-Pro
Multimodal
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
- Claude Opus 4.5
- $0.0175
- Fits in one request
- Pareto 26.9
- $0.00625
- Fit state unavailable
Pareto 26.9 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Opus 4.5
- $0.325
- Fits in one request
- Pareto 26.9
- $0.1475
- Fit state unavailable
Pareto 26.9 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Opus 4.5
- $1.35
- Does not fit in one request
- Cached input priced at the published list-input rate
- Pareto 26.9
- $0.175
- Fit state unavailable
Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Opus 4.5Pareto 26.9
Context window
Maximum documented context; output-token limits may be lower.
Claude Opus 4.5
200K
Pareto 26.9
N/A
API model ID
Claude Opus 4.5
Not sourced
Pareto 26.9
pareto
Unbiased pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Opus 4.5
Not published
Pareto 26.9
$0.25 per 1M cached input tokens
Unbiased pricingDocumented inputs
Claude Opus 4.5
Not sourced
Pareto 26.9
Not sourced
Documented outputs
Claude Opus 4.5
Not sourced
Pareto 26.9
Not sourced
Provider availability
Claude Opus 4.5
Not sourced
Pareto 26.9
Not sourced
Reasoning profile
Claude Opus 4.5
Non-Reasoning
Pareto 26.9
Reasoning
Weight access
Claude Opus 4.5
Proprietary
Pareto 26.9
Proprietary
License
Claude Opus 4.5
Proprietary
Pareto 26.9
Proprietary
Release date
Claude Opus 4.5
2025-11-01
Pareto 26.9
2026-09-17
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: $0.325 vs $0.1475. Cache-heavy agent loop: $1.35 vs $0.175.
- Context tradeoff
- A complete documented context comparison is not available.
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 evidence48 rows
Agentic
Terminal-Bench 2.0
Claude Opus 4.559.3%SourcePareto 26.9—Not directly comparable
OSWorld-Verified
Claude Opus 4.566.3%SourcePareto 26.9—Not directly comparable
OSWorld
Claude Opus 4.566.3%SourcePareto 26.9—Not directly comparable
Claw-Eval
Claude Opus 4.559.6%SourcePareto 26.9—Not directly comparable
QwenClawBench
Claude Opus 4.552.3%SourcePareto 26.9—Not directly comparable
τ³-bench results
Claude Opus 4.570.2%SourcePareto 26.9—Not directly comparable
VITA-Bench
Claude Opus 4.523.3%SourcePareto 26.9—Not directly comparable
DeepPlanning
Claude Opus 4.526.4%SourcePareto 26.9—Not directly comparable
Toolathlon
Claude Opus 4.543.5%SourcePareto 26.9—Not directly comparable
MCP Atlas
Claude Opus 4.542.3%SourcePareto 26.9—Not directly comparable
MCP-Tasks
Claude Opus 4.571.8%SourcePareto 26.9—Not directly comparable
WideResearch
Claude Opus 4.576.4%SourcePareto 26.9—Not directly comparable
CyberGym
Claude Opus 4.550.6%SourcePareto 26.9—Not directly comparable
Gert Labs
Claude Opus 4.564.23%SourcePareto 26.9—Not directly comparable
JobBench
Claude Opus 4.532.3%SourcePareto 26.9—Not directly comparable
Terminal-Bench 4.0
Claude Opus 4.5—Pareto 26.951.00%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Opus 4.580.9%SourcePareto 26.9—Not directly comparable
LiveCodeBench v6
Claude Opus 4.584.8%SourcePareto 26.9—Not directly comparable
SWE-bench Pro
Claude Opus 4.557.1%SourcePareto 26.9—Not directly comparable
SWE Multilingual
Claude Opus 4.577.5%SourcePareto 26.9—Not directly comparable
NL2Repo
Claude Opus 4.543.2%SourcePareto 26.9—Not directly comparable
DeepSWE
Claude Opus 4.5—Pareto 26.974.0%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Claude Opus 4.587%SourcePareto 26.9—Not directly comparable
SuperGPQA
Claude Opus 4.570.6%SourcePareto 26.9—Not directly comparable
MMLU-Pro
Claude Opus 4.589.5%SourcePareto 26.9—Not directly comparable
MMLU-Redux
Claude Opus 4.596.6%SourcePareto 26.9—Not directly comparable
C-Eval
Claude Opus 4.592.2%SourcePareto 26.9—Not directly comparable
HLE
Claude Opus 4.530.8%SourcePareto 26.9—Not directly comparable
HLE w/o tools
Claude Opus 4.5—Pareto 26.949%SourceNot directly comparable
Math
AIME26
Claude Opus 4.595.1%SourcePareto 26.9—Not directly comparable
HMMT Feb 2025
Claude Opus 4.592.9%SourcePareto 26.9—Not directly comparable
HMMT Nov 2025
Claude Opus 4.593.3%SourcePareto 26.9—Not directly comparable
HMMT Feb 2026
Claude Opus 4.585.3%SourcePareto 26.9—Not directly comparable
MMAnswerBench
Claude Opus 4.584.0%SourcePareto 26.9—Not directly comparable
FrontierMath v2 (Tiers 1-3)
Claude Opus 4.520.690%SourcePareto 26.9—Not directly comparable
FrontierMath v2 (Tier 4)
Claude Opus 4.54.167%SourcePareto 26.9—Not directly comparable
Multilingual
Multimodal
MMMU-Pro
Claude Opus 4.570.6%SourcePareto 26.978%SourcePareto 26.9 leads this result
MathVision
Claude Opus 4.574.3%SourcePareto 26.9—Not directly comparable
CharXiv
Claude Opus 4.568.5%SourcePareto 26.9—Not directly comparable
VideoMMMU
Claude Opus 4.584.4%SourcePareto 26.9—Not directly comparable
ScreenSpot Pro
Claude Opus 4.545.7%SourcePareto 26.9—Not directly comparable
V*
Claude Opus 4.567.0%SourcePareto 26.9—Not directly comparable
Instruction following
Questions
Which is better, Claude Opus 4.5 or Pareto 26.9?
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, Claude Opus 4.5 or Pareto 26.9?
Pareto 26.9 is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Opus 4.5 or Pareto 26.9?
Pareto 26.9 is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Opus 4.5 or Pareto 26.9?
For the stated presets, chat costs $0.0175 on Claude Opus 4.5 and $0.00625 on Pareto 26.9; repository review costs $0.325 and $0.1475; the cache-heavy agent loop costs $1.35 and $0.175. Claude Opus 4.5 does not fit this workload in one request. Claude Opus 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Claude Opus 4.5 or Pareto 26.9?
A complete documented context-window comparison is not available.
Related comparisons
Last updated September 18, 2026
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