Agentic
Like-for-like- Gemini 3.1 Pro
- 40.1
- Supported · #120/151
- Ling 3.0 Flash
- 40.0
- Supported · #121/151
- Basis
- BenchAlign lane · 6 vs 7 public rows
- Reading
- Practical tie
Gemini 3.1 Pro vs Ling 3.0 Flash
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.1 Pro has the higher public score estimate, 69.82 versus 52.2, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Share or export
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.
Long documents
Prompts that approach the documented context limit
Gemini 3.1 Pro
Gemini 3.1 Pro has the larger documented context window.
Confidence: documented
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Ling 3.0 Flash is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
No clear pick
The like-for-like agentic result is a practical tie on the public lane (within 0.5 points).
Confidence: limited
Chat 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-rates
Cache-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-rates
Repository 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
22 Gemini 3.1 Pro only
6 Shared
16 Ling 3.0 Flash only
- Shared results
- 6
- Gemini 3.1 Pro only
- 22
- Ling 3.0 Flash only
- 16
- Like-for-like categories
- 2 / 8
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
Knowledge
Like-for-like- Gemini 3.1 Pro
- 65.6
- Supported · #24/181
- Ling 3.0 Flash
- 45.9
- Supported · #112/181
- Basis
- BenchAlign lane · 6 vs 5 public rows
- Reading
- Gemini 3.1 Pro leads · intervals overlap
Coding
Directional only- Gemini 3.1 Pro
- 46.3
- Supported · #99/183
- Ling 3.0 Flash
- 42.8
- Estimated · #126/183
- Basis
- BenchAlign lane · 5 vs 6 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.1 Pro
- 50.7
- Unranked · 2 rankable rows
- Ling 3.0 Flash
- 69.2
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.1 Pro
- 54.3
- Unranked · 2 rankable rows
- Ling 3.0 Flash
- 73.7
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 2 vs 2 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.1 Pro
- Not ranked
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.1 Pro
- 79.2
- #12/48
- Ling 3.0 Flash
- Not ranked
- Basis
- Provisional lane · 2 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.1 Pro
- Not ranked
- Ling 3.0 Flash
- 75.6
- #58/120
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.1 Pro | Ling 3.0 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 40.1Supported · #120/151 | 40.0Supported · #121/151 | Like-for-likeBenchAlign lane · 6 vs 7 public rows | Practical tie |
| Knowledge | 65.6Supported · #24/181 | 45.9Supported · #112/181 | Like-for-likeBenchAlign lane · 6 vs 5 public rows | Gemini 3.1 Pro leads · intervals overlap |
| Coding | 46.3Supported · #99/183 | 42.8Estimated · #126/183 | Directional onlyBenchAlign lane · 5 vs 6 public rows | Directional only |
| Reasoning | 50.7Unranked · 2 rankable rows | 69.2Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Math | 54.3Unranked · 2 rankable rows | 73.7Unranked · 3 rankable rows | Not comparableProvisional lane · 2 vs 2 weighted rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 79.2#12/48 | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 75.6#58/120 | 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) 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.
MMLU-Pro (Vals)
Knowledge
Gemini 3.1 Pro: 91.0%Ling 3.0 Flash: 82.0%Normalized gap 9.0Gemini 3.1 Pro source Ling 3.0 Flash sourceLiveCodeBench (Vals)
Coding
Gemini 3.1 Pro: 88.5%Ling 3.0 Flash: 84.0%Normalized gap 4.5Gemini 3.1 Pro source Ling 3.0 Flash source
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
- Gemini 3.1 Pro
- $0.008
- Fits in one request
- Ling 3.0 Flash
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.1 Pro
- $0.136
- Fits in one request
- Ling 3.0 Flash
- API rate not published
- Fits in one request
Ling 3.0 Flash has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.1 Pro
- $0.2
- Fits in one request
- Ling 3.0 Flash
- API rate not published
- Fits in one request
- Cached-input rate unavailable
Ling 3.0 Flash has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.1 ProLing 3.0 Flash
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.1 Pro
Ling 3.0 Flash
API model ID
Gemini 3.1 Pro
gemini-3.1-pro-preview
Google Gemini 3.1 Pro Preview model documentationLing 3.0 Flash
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.1 Pro
$0.2 per 1M cached input tokens
Google Gemini API pricingLing 3.0 Flash
No comparable hosted API rate
InclusionAI Ling 3.0 Flash model cardDocumented inputs
Gemini 3.1 Pro
text, image, video, audio, pdf
Google Gemini 3.1 Pro Preview model documentationLing 3.0 Flash
Not sourced
Documented outputs
Gemini 3.1 Pro
Ling 3.0 Flash
Not sourced
Provider availability
Gemini 3.1 Pro
Preview · Gemini API, Google AI Studio
Google Gemini model catalogLing 3.0 Flash
Not sourced
Reasoning profile
Gemini 3.1 Pro
Reasoning
Ling 3.0 Flash
Reasoning
Weight access
Gemini 3.1 Pro
Proprietary
Ling 3.0 Flash
Open Weight
License
Gemini 3.1 Pro
Proprietary
Ling 3.0 Flash
Open Weight
Release date
Gemini 3.1 Pro
2026-02-19
Ling 3.0 Flash
2026-07-23
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
- Gemini 3.1 Pro has the higher public score estimate, 69.82 versus 52.2, but the 90% score intervals overlap.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Gemini 3.1 Pro has the larger documented window (1M).
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 evidence44 rows
Agentic
Claw-Eval
Gemini 3.1 Pro57.8%SourceLing 3.0 Flash—Not directly comparable
DeepSearchQA
Gemini 3.1 Pro69.7%SourceLing 3.0 Flash—Not directly comparable
τ²-bench results
Gemini 3.1 Pro95.6%SourceLing 3.0 Flash—Not directly comparable
Gert Labs
Gemini 3.1 Pro56.87%SourceLing 3.0 Flash—Not directly comparable
ResearchClawBench
Gemini 3.1 Pro13.3%SourceLing 3.0 Flash—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.1 Pro70.8%SourceLing 3.0 Flash50.2%SourceGemini 3.1 Pro leads this result
MCP Atlas
Gemini 3.1 Pro—Ling 3.0 Flash65.5%SourceNot directly comparable
skillsBench
Gemini 3.1 Pro—Ling 3.0 Flash44.8%SourceNot directly comparable
BFCL v4
Gemini 3.1 Pro—Ling 3.0 Flash73.0%SourceNot directly comparable
WideResearch
Gemini 3.1 Pro—Ling 3.0 Flash73.6%SourceNot directly comparable
BrowseComp
Gemini 3.1 Pro—Ling 3.0 Flash72.2%SourceNot directly comparable
DRACO
Gemini 3.1 Pro—Ling 3.0 Flash70.4%SourceNot directly comparable
Coding
LiveCodeBench Pro
Gemini 3.1 Pro82.9%SourceLing 3.0 Flash—Not directly comparable
React Native Evals
Gemini 3.1 Pro78.9%SourceLing 3.0 Flash—Not directly comparable
Vibe Code Bench
Gemini 3.1 Pro32.03%SourceLing 3.0 Flash—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.1 Pro88.5%SourceLing 3.0 Flash84.0%SourceGemini 3.1 Pro leads this result
SWE-bench (Vals)
Gemini 3.1 Pro78.8%SourceLing 3.0 Flash65.2%SourceGemini 3.1 Pro leads this result
SWE-bench Pro
Gemini 3.1 Pro—Ling 3.0 Flash56.6%SourceNot directly comparable
SWE Multilingual
Gemini 3.1 Pro—Ling 3.0 Flash72.4%SourceNot directly comparable
LiveCodeBench v5
Gemini 3.1 Pro—Ling 3.0 Flash82.8%SourceNot directly comparable
SciCode
Gemini 3.1 Pro—Ling 3.0 Flash41.2%SourceNot directly comparable
Reasoning
Knowledge
GPQA-D
Gemini 3.1 Pro94.3%SourceLing 3.0 Flash85.0%SourceGemini 3.1 Pro leads this result
HLE w/o tools
Gemini 3.1 Pro45.4%SourceLing 3.0 Flash—Not directly comparable
HealthBench Hard
Gemini 3.1 Pro20.6%SourceLing 3.0 Flash—Not directly comparable
MedXpertQA (Text)
Gemini 3.1 Pro71.5%SourceLing 3.0 Flash—Not directly comparable
GPQA Diamond (Vals)
Gemini 3.1 Pro95.5%SourceLing 3.0 Flash84.8%SourceGemini 3.1 Pro leads this result
MMLU-Pro (Vals)
Gemini 3.1 Pro91.0%SourceLing 3.0 Flash82.0%SourceGemini 3.1 Pro leads this result
GPQA
Gemini 3.1 Pro—Ling 3.0 Flash85.0%SourceNot directly comparable
HLE
Gemini 3.1 Pro—Ling 3.0 Flash22.7%SourceNot directly comparable
Math
FrontierMath v2 (Tiers 1-3)
Gemini 3.1 Pro36.900%SourceLing 3.0 Flash—Not directly comparable
FrontierMath v2 (Tier 4)
Gemini 3.1 Pro16.700%SourceLing 3.0 Flash—Not directly comparable
AIME26
Gemini 3.1 Pro—Ling 3.0 Flash93.2%SourceNot directly comparable
HMMT Feb 2026
Gemini 3.1 Pro—Ling 3.0 Flash87.0%SourceNot directly comparable
IMOAnswerBench
Gemini 3.1 Pro—Ling 3.0 Flash83.7%SourceNot directly comparable
Multimodal
MMMU-Pro
Gemini 3.1 Pro83.9%SourceLing 3.0 Flash—Not directly comparable
CharXiv
Gemini 3.1 Pro80.2%SourceLing 3.0 Flash—Not directly comparable
ERQA
Gemini 3.1 Pro69.4%SourceLing 3.0 Flash—Not directly comparable
SimpleVQA
Gemini 3.1 Pro72.4%SourceLing 3.0 Flash—Not directly comparable
ScreenSpot Pro
Gemini 3.1 Pro84.4%SourceLing 3.0 Flash—Not directly comparable
ZeroBench
Gemini 3.1 Pro29.0%SourceLing 3.0 Flash—Not directly comparable
MedXpertQA (MM)
Gemini 3.1 Pro81.3%SourceLing 3.0 Flash—Not directly comparable
Instruction following
IFBench
Gemini 3.1 Pro—Ling 3.0 Flash74.5%SourceNot directly comparable
Frequently asked questions
Which is better, Gemini 3.1 Pro or Ling 3.0 Flash?
Gemini 3.1 Pro has the higher public score estimate, 69.82 versus 52.2, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Gemini 3.1 Pro or Ling 3.0 Flash?
Gemini 3.1 Pro scores higher for coding on the public lane, 46.3 to 42.8. Ling 3.0 Flash is 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, Gemini 3.1 Pro or Ling 3.0 Flash?
The like-for-like agentic tasks row is a practical tie on the public lane, 40.1 against 40, inside the 0.5-point band BenchLM treats as level.
Which costs less, Gemini 3.1 Pro or Ling 3.0 Flash?
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, Gemini 3.1 Pro or Ling 3.0 Flash?
Gemini 3.1 Pro has the larger documented context window: 1M, compared with 262K.
Related comparisons
Last updated September 4, 2026
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