Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Beam is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Claude Fable 5.1 vs Beam
Updated October 5, 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
Share or export
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. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 4
- Claude Fable 5.1 only
- 26
- Beam only
- 12
- Like-for-like categories
- 0 / 8
Supported: Claude Fable 5.1How 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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Beam is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedLong documents
Prompts that approach the documented context limit
Not enough matched evidence
A complete context comparison is not sourced.
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
A complete comparable API-rate estimate is not available for both models.
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.
77.8Claude Fable 5.1—Beam
Not comparable · BenchAlign v5.8
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.
26 Claude Fable 5.1 only
4 Shared
12 Beam 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.
HLE w/o toolsKnowledge
Normalized gap 24.7Claude Fable 5.1:60.9%Beam:36.2%SWE-bench ProCoding
Normalized gap 15.7Claude Fable 5.1:81.2%Beam:65.5%SWE MultilingualCoding
Normalized gap 11.1Claude Fable 5.1:89.1%Beam:78%
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.
Agentic
Not comparable- Claude Fable 5.1
- 79.0
- Supported · #2/119
- Beam
- Not ranked
- Basis
- BenchAlign v5.8 lane · 11 vs 4 public rows
- Reading
- Not comparable
Coding
Not comparable- Claude Fable 5.1
- 77.8
- Supported · #3/144
- Beam
- Not ranked
- Basis
- BenchAlign v5.8 lane · 11 vs 7 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Claude Fable 5.1
- 81.4
- #4/27
- Beam
- Not ranked
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Fable 5.1
- Not ranked
- Beam
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Claude Fable 5.1
- 84.9
- Supported · #2/171
- Beam
- Not ranked
- Basis
- BenchAlign v5.8 lane · 4 vs 2 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Fable 5.1
- Not ranked
- Beam
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Fable 5.1
- Not ranked
- Beam
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Fable 5.1
- Not ranked
- Beam
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Claude Fable 5.1 | Beam | Basis | Reading |
|---|---|---|---|---|
| Agentic | 79.0Supported · #2/119 | Not ranked | Not comparableBenchAlign v5.8 lane · 11 vs 4 public rows | Not comparable |
| Coding | 77.8Supported · #3/144 | Not ranked | Not comparableBenchAlign v5.8 lane · 11 vs 7 public rows | Not comparable |
| Reasoning | 81.4#4/27 | Not ranked | Not comparableProvisional lane · 1 vs 1 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 84.9Supported · #2/171 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 2 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 1 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
- Claude Fable 5.1
- $0.035
- Fits in one request
- Beam
- API rate not published
- Fit state unavailable
Beam has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Claude Fable 5.1
- $0.65
- Fits in one request
- Beam
- API rate not published
- Fit state unavailable
Beam has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Fable 5.1
- $0.75
- Fits in one request
- Beam
- API rate not published
- Fit state unavailable
- Cached-input rate unavailable
Beam 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.
SpecificationClaude Fable 5.1Beam
Context window
Maximum documented context; output-token limits may be lower.
Claude Fable 5.1
Beam
Not sourced
Reflection AI Beam announcementAPI model ID
Claude Fable 5.1
claude-fable-5-1
Anthropic Fable 5.1 and Mythos 5.1 launchBeam
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Fable 5.1
$0.25 per 1M cached input tokens
Anthropic Fable 5.1 launchBeam
No comparable hosted API rate
Reflection AI Beam announcementDocumented inputs
Claude Fable 5.1
Not sourced
Beam
Documented outputs
Claude Fable 5.1
Not sourced
Beam
Provider availability
Claude Fable 5.1
Generally Available · Claude API, Claude products, AWS, Google Cloud, Microsoft Azure
Anthropic Fable 5.1 and Mythos 5.1 launchBeam
Limited Access · Reflection early-access waitlist
Reflection AI Beam announcementReasoning profile
Claude Fable 5.1
Reasoning
Beam
Reasoning
Weight access
Claude Fable 5.1
Proprietary
Beam
Pending
License
Claude Fable 5.1
Proprietary
Beam
Pending
Release date
Claude Fable 5.1
2026-09-01
Beam
2026-10-05
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
- 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
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- A complete documented context comparison is not available.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Claude Fable 5.1 or Beam?
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 Fable 5.1 or Beam?
Beam is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, Claude Fable 5.1 or Beam?
Beam is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Claude Fable 5.1 or Beam?
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, Claude Fable 5.1 or Beam?
A complete documented context-window comparison is not available.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence42 rows
Agentic
Terminal-Bench 4.0
Claude Fable 5.155.80%SourceBeam—Not directly comparable
Terminal-Bench-Science 0.1
Claude Fable 5.152.6%SourceBeam—Not directly comparable
OSWorld 2.0
Claude Fable 5.141.7%SourceBeam—Not directly comparable
AutomationBench
Claude Fable 5.131.4%SourceBeam—Not directly comparable
Toolathlon-Verified
Claude Fable 5.177.8%SourceBeam—Not directly comparable
Toolathlon Verified Pass@3
Claude Fable 5.181.5%SourceBeam—Not directly comparable
Toolathlon Verified Pass³
Claude Fable 5.173.1%SourceBeam—Not directly comparable
Toolathlon Verified avg. turns
Claude Fable 5.123.7 turnsSourceBeam—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Fable 5.185.0%SourceBeam—Not directly comparable
ApprenticeBench
Claude Fable 5.172%SourceBeam—Not directly comparable
CWE-bench v1
Claude Fable 5.158.0%SourceBeam—Not directly comparable
Terminal-Bench 2.1
Claude Fable 5.1—Beam80.1%SourceNot directly comparable
MCP Atlas
Claude Fable 5.1—Beam78.7%SourceNot directly comparable
BrowseComp
Claude Fable 5.1—Beam77.4%SourceNot directly comparable
DeepSearchQA
Claude Fable 5.1—Beam80.1%SourceNot directly comparable
Coding
Bug Hunt Bench
Claude Fable 5.143 fixesSourceBeam—Not directly comparable
SWE-bench Pro
Claude Fable 5.181.2%SourceBeam65.5%SourceClaude Fable 5.1 leads this result
SWE Multilingual
Claude Fable 5.189.1%SourceBeam78%SourceClaude Fable 5.1 leads this result
SWE Multimodal
Claude Fable 5.154.7%SourceBeam—Not directly comparable
DeepSWE
Claude Fable 5.167.4%SourceBeam44.4%SourceClaude Fable 5.1 leads this result
FrontierSWE v2
Claude Fable 5.156.3%SourceBeam—Not directly comparable
ProgramBench
Claude Fable 5.187.6%SourceBeam—Not directly comparable
CursorBench 3.2
Claude Fable 5.173.4%SourceBeam—Not directly comparable
LiveCodeBench (Vals)
Claude Fable 5.190.5%SourceBeam—Not directly comparable
CursorBench 4.0
Claude Fable 5.151.8%SourceBeam—Not directly comparable
PostTrainBench v1.1
Claude Fable 5.140.2%SourceBeam—Not directly comparable
Terminal-Bench 2.1
Claude Fable 5.1—Beam80.1%SourceNot directly comparable
SWE-Atlas Codebase QnA
Claude Fable 5.1—Beam34.6%SourceNot directly comparable
SWE-bench Verified
Claude Fable 5.1—Beam80.9%SourceNot directly comparable
SciCode
Claude Fable 5.1—Beam49.7%SourceNot directly comparable
Reasoning
Knowledge
HLE
Claude Fable 5.165%SourceBeam—Not directly comparable
HLE w/o tools
Claude Fable 5.160.9%SourceBeam36.2%SourceClaude Fable 5.1 leads this result
GPQA Diamond (Vals)
Claude Fable 5.193.4%SourceBeam—Not directly comparable
MMLU-Pro (Vals)
Claude Fable 5.192.4%SourceBeam—Not directly comparable
GPQA-D
Claude Fable 5.1—Beam90.5%SourceNot directly comparable
Instruction following
Math
AIME26
Claude Fable 5.1—Beam97.8%SourceNot directly comparable
42 public results · 4 shared
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Last updated October 5, 2026