Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.1 Flash and Sage-A1.5 are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Ling 3.1 Flash vs Sage-A1.5
Updated October 20, 2026. Rank says Ling 3.1 Flash is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Share or export
Decision reading
Ling 3.1 Flash has the higher public point estimate, 68.05 versus 62.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- Ling 3.1 Flash only
- 14
- Sage-A1.5 only
- 9
- Like-for-like categories
- 0 / 8
Estimated: Ling 3.1 Flash and Sage-A1.5. Conditional ranges do not establish rank confidence.How 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
Ling 3.1 Flash and Sage-A1.5 are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limitedLong documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documentedChat 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.
57.2Ling 3.1 Flash54.0Sage-A1.5
Directional only · BenchAlign v5.8
Ling 3.1 Flash 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.
14 Ling 3.1 Flash only
3 Shared
9 Sage-A1.5 only
2 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.
SWE MultilingualCoding
Normalized gap 15.8Ling 3.1 Flash:88.5%Sage-A1.5:72.7%BrowseCompAgentic
Normalized gap 10.0Ling 3.1 Flash:91.7%Sage-A1.5:81.7%SWE-bench ProCoding
Normalized gap 7.5Ling 3.1 Flash:68.7%Sage-A1.5:61.2%
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
Directional only- Ling 3.1 Flash
- 62.8
- Estimated · #22/132
- Sage-A1.5
- 57.7
- Estimated · #36/132
- Basis
- BenchAlign v5.8 lane · 10 vs 6 public rows
- Reading
- Directional only
Coding
Directional only- Ling 3.1 Flash
- 57.2
- Estimated · #27/153
- Sage-A1.5
- 54.0
- Estimated · #34/153
- Basis
- BenchAlign v5.8 lane · 5 vs 3 public rows
- Reading
- Directional only
Reasoning
Not comparable- Ling 3.1 Flash
- 82.0
- Unranked · 2 rankable rows
- Sage-A1.5
- 80.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Ling 3.1 Flash
- Not ranked
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Ling 3.1 Flash
- 63.4
- Supported · #43/187
- Sage-A1.5
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Ling 3.1 Flash
- Not ranked
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Ling 3.1 Flash
- Not ranked
- Sage-A1.5
- 91.9
- #22/152
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
Math
Not comparable- Ling 3.1 Flash
- Not ranked
- Sage-A1.5
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Ling 3.1 Flash | Sage-A1.5 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 62.8Estimated · #22/132 | 57.7Estimated · #36/132 | Directional onlyBenchAlign v5.8 lane · 10 vs 6 public rows | Directional only |
| Coding | 57.2Estimated · #27/153 | 54.0Estimated · #34/153 | Directional onlyBenchAlign v5.8 lane · 5 vs 3 public rows | Directional only |
| Reasoning | 82.0Unranked · 2 rankable rows | 80.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 63.4Supported · #43/187 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 91.9#22/152 | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 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
- Ling 3.1 Flash
- API rate not published
- Fits in one request
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.1 Flash has no comparable published API token rate. Sage-A1.5 has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Ling 3.1 Flash
- API rate not published
- Fits in one request
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
Ling 3.1 Flash has no comparable published API token rate. Sage-A1.5 has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Ling 3.1 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Sage-A1.5
- Self-hosted; infrastructure cost varies
- Fits in one request
- Cached-input rate unavailable
Ling 3.1 Flash has no comparable published API token rate. Sage-A1.5 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.
SpecificationLing 3.1 FlashSage-A1.5
Context window
Maximum documented context; output-token limits may be lower.
Ling 3.1 Flash
Sage-A1.5
API model ID
Ling 3.1 Flash
inclusionai/ling-3.1-flash
Vercel AI Gateway Ling 3.1 Flash routeSage-A1.5
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Ling 3.1 Flash
No comparable hosted API rate
Vercel AI Gateway availabilitySage-A1.5
No comparable hosted API rate
InternScience Sage-A1.5 model cardDocumented inputs
Ling 3.1 Flash
Sage-A1.5
Not sourced
Documented outputs
Ling 3.1 Flash
Sage-A1.5
Not sourced
Provider availability
Ling 3.1 Flash
Generally Available · Hugging Face weights, SGLang, vLLM, Vercel AI Gateway API via Novita
Vercel AI Gateway Ling 3.1 Flash routeSage-A1.5
Not sourced
Reasoning profile
Ling 3.1 Flash
Reasoning
Sage-A1.5
Reasoning
Weight access
Ling 3.1 Flash
Open Weight
Sage-A1.5
Open Weight
License
Ling 3.1 Flash
Open Weight
Sage-A1.5
Open Weight
Release date
Ling 3.1 Flash
2026-09-29
Sage-A1.5
2026-10-15
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
- Ling 3.1 Flash has the higher public point estimate, 68.05 versus 62.95. Their conditional score ranges overlap. These ranges do not establish rank confidence.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 262K.
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Ling 3.1 Flash or Sage-A1.5?
Ling 3.1 Flash has the higher public point estimate, 68.05 versus 62.95. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Ling 3.1 Flash or Sage-A1.5?
Ling 3.1 Flash scores higher for coding on the public lane, 57.2 to 54. Ling 3.1 Flash and Sage-A1.5 are 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, Ling 3.1 Flash or Sage-A1.5?
Ling 3.1 Flash scores higher for agentic tasks on the public lane, 62.8 to 57.7. Ling 3.1 Flash and Sage-A1.5 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, Ling 3.1 Flash or Sage-A1.5?
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, Ling 3.1 Flash or Sage-A1.5?
Both models list the same context window, 262K.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Ling 3.1 Flash
API / mo$0
Self-host / mo$27,962
Break-even—
Sage-A1.5
API / mo$0
Self-host / mo$298
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 evidence26 rows
Agentic
skillsBench
Ling 3.1 Flash68.7%SourceSage-A1.5—Not directly comparable
AutomationBench
Ling 3.1 Flash52.5%SourceSage-A1.5—Not directly comparable
CyberGym
Ling 3.1 Flash87.9%SourceSage-A1.5—Not directly comparable
Finance Agent v2
Ling 3.1 Flash57.9%SourceSage-A1.5—Not directly comparable
DRACO
Ling 3.1 Flash85.5%SourceSage-A1.5—Not directly comparable
Terminal-Bench 2.1
Ling 3.1 Flash81.2%SourceSage-A1.5—Not directly comparable
τ³-bench results
Ling 3.1 Flash47.8%SourceSage-A1.5—Not directly comparable
MCP Atlas
Ling 3.1 Flash83%SourceSage-A1.5—Not directly comparable
BrowseComp
Ling 3.1 Flash91.7%SourceSage-A1.581.7%SourceLing 3.1 Flash leads this result
WideResearch
Ling 3.1 Flash83.3%SourceSage-A1.5—Not directly comparable
GAIA
Ling 3.1 Flash—Sage-A1.595.1%SourceNot directly comparable
MLE-Bench Lite
Ling 3.1 Flash—Sage-A1.565.2%SourceNot directly comparable
HLE w/ tools
Ling 3.1 Flash—Sage-A1.555.1%SourceNot directly comparable
VITA-Bench
Ling 3.1 Flash—Sage-A1.545.4%SourceNot directly comparable
PinchBench
Ling 3.1 Flash—Sage-A1.590.0%SourceNot directly comparable
Coding
terminalBench4
Ling 3.1 Flash46.0%SourceSage-A1.5—Not directly comparable
SWE-Atlas Codebase QnA
Ling 3.1 Flash64.5%SourceSage-A1.5—Not directly comparable
SWE Multilingual
Ling 3.1 Flash88.5%SourceSage-A1.572.7%SourceLing 3.1 Flash leads this result
SWE-bench Pro
Ling 3.1 Flash68.7%SourceSage-A1.561.2%SourceLing 3.1 Flash leads this result
DeepSWE
Ling 3.1 Flash59.7%SourceSage-A1.5—Not directly comparable
SciCode
Ling 3.1 Flash—Sage-A1.544.3%SourceNot directly comparable
Reasoning
CritPt
Ling 3.1 Flash—Sage-A1.520.0%SourceNot directly comparable
Knowledge
Instruction following
IFBench
Ling 3.1 Flash—Sage-A1.581.6%SourceNot directly comparable
26 public results · 3 shared
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Last updated October 20, 2026