Agentic
Like-for-like- Claude Sonnet 5
- 65.8
- Supported · #11/152
- Gemini 3.7 Flash
- 64.0
- Supported · #12/152
- Basis
- BenchAlign lane · 6 vs 6 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Claude Sonnet 5 vs Gemini 3.7 Flash
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Sonnet 5 has the higher public score estimate, 69.84 versus 68.49, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.
11 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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Sonnet 5
Claude Sonnet 5 leads on the public coding lane, 64 to 62.7, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Claude Sonnet 5
Claude Sonnet 5 leads on the public agentic lane, 65.8 to 64, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash 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
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
Gemini 3.7 Flash
Gemini 3.7 Flash has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
13 Claude Sonnet 5 only
11 Shared
11 Gemini 3.7 Flash only
- Shared results
- 11
- Claude Sonnet 5 only
- 13
- Gemini 3.7 Flash only
- 11
- Like-for-like categories
- 4 / 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
Like-for-like- Claude Sonnet 5
- 64.0
- Supported · #13/151
- Gemini 3.7 Flash
- 62.7
- Supported · #15/151
- Basis
- BenchAlign lane · 10 vs 6 public rows
- Reading
- Claude Sonnet 5 leads · intervals overlap
Knowledge
Like-for-like- Claude Sonnet 5
- 66.6
- Supported · #20/183
- Gemini 3.7 Flash
- 69.7
- Supported · #13/183
- Basis
- BenchAlign lane · 6 vs 6 public rows
- Reading
- Gemini 3.7 Flash leads · intervals overlap
Multimodal
Like-for-like- Claude Sonnet 5
- 77.5
- #13/48
- Gemini 3.7 Flash
- 82.7
- #9/48
- Basis
- Provisional lane · 1 vs 1 weighted rows
- Reading
- Gemini 3.7 Flash leads
Reasoning
Not comparable- Claude Sonnet 5
- 77.4
- Unranked · 2 rankable rows
- Gemini 3.7 Flash
- 77.2
- Unranked · 3 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Claude Sonnet 5
- Not ranked
- Gemini 3.7 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Sonnet 5
- Not ranked
- Gemini 3.7 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Sonnet 5
- Not ranked
- Gemini 3.7 Flash
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Claude Sonnet 5 | Gemini 3.7 Flash | Basis | Reading |
|---|---|---|---|---|
| Agentic | 65.8Supported · #11/152 | 64.0Supported · #12/152 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | Claude Sonnet 5 leads · intervals overlap |
| Coding | 64.0Supported · #13/151 | 62.7Supported · #15/151 | Like-for-likeBenchAlign lane · 10 vs 6 public rows | Claude Sonnet 5 leads · intervals overlap |
| Knowledge | 66.6Supported · #20/183 | 69.7Supported · #13/183 | Like-for-likeBenchAlign lane · 6 vs 6 public rows | Gemini 3.7 Flash leads · intervals overlap |
| Multimodal | 77.5#13/48 | 82.7#9/48 | Like-for-likeProvisional lane · 1 vs 1 weighted rows | Gemini 3.7 Flash leads |
| Reasoning | 77.4Unranked · 2 rankable rows | 77.2Unranked · 3 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted 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 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.
LiveCodeBench (Vals)
Coding
Claude Sonnet 5: 82.4%Gemini 3.7 Flash: 88.7%Normalized gap 6.3Claude Sonnet 5 source Gemini 3.7 Flash sourceMMLU-Pro (Vals)
Knowledge
Claude Sonnet 5: 87.5%Gemini 3.7 Flash: 90.1%Normalized gap 2.6Claude Sonnet 5 source Gemini 3.7 Flash sourceCharXiv
Multimodal
Claude Sonnet 5: 88.3%Gemini 3.7 Flash: 88.7%Normalized gap 0.4Claude Sonnet 5 source Gemini 3.7 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
- Claude Sonnet 5
- $0.007
- Fits in one request
- Gemini 3.7 Flash
- $0.00262
- Fits in one request
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Sonnet 5
- $0.13
- Fits in one request
- Gemini 3.7 Flash
- $0.04875
- Fits in one request
Gemini 3.7 Flash 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 Sonnet 5
- $0.18
- Fits in one request
- Gemini 3.7 Flash
- $0.0675
- Fits in one request
Gemini 3.7 Flash has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Sonnet 5Gemini 3.7 Flash
Context window
Maximum documented context; output-token limits may be lower.
Claude Sonnet 5
Gemini 3.7 Flash
API model ID
Claude Sonnet 5
claude-sonnet-5
Anthropic model overviewGemini 3.7 Flash
gemini-3.7-flash
Google Gemini 3.7 Flash API documentationCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 5
$0.2 per 1M cached input tokens
Claude API pricingGemini 3.7 Flash
$0.075 per 1M cached input tokens
Google Gemini API pricingDocumented inputs
Claude Sonnet 5
text, image
Anthropic model overviewGemini 3.7 Flash
text, image, video, audio, pdf
Google Gemini 3.7 Flash API documentationDocumented outputs
Claude Sonnet 5
Gemini 3.7 Flash
Provider availability
Claude Sonnet 5
Generally Available · Claude API
Anthropic model overviewGemini 3.7 Flash
Generally Available · Gemini API, Google AI Studio, Gemini App - Spark, Gemini Enterprise App, Gemini Enterprise Agent Platform, Google Antigravity
Google DeepMind Gemini 3.7 Flash model cardReasoning profile
Claude Sonnet 5
Reasoning
Gemini 3.7 Flash
Reasoning
Weight access
Claude Sonnet 5
Proprietary
Gemini 3.7 Flash
Proprietary
License
Claude Sonnet 5
Proprietary
Gemini 3.7 Flash
Proprietary
Release date
Claude Sonnet 5
2026-06-30
Gemini 3.7 Flash
2026-08-13
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
- Claude Sonnet 5 has the higher public score estimate, 69.84 versus 68.49, but the 90% score intervals overlap.
- Workload cost
- Repository review: $0.13 vs $0.04875. Cache-heavy agent loop: $0.18 vs $0.0675.
- Context tradeoff
- Both models list 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 evidence35 rows
Agentic
Terminal-Bench 3.0
Claude Sonnet 514.6%SourceGemini 3.7 Flash14.9%SourceGemini 3.7 Flash leads this result
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGemini 3.7 Flash—Not directly comparable
BrowseComp
Claude Sonnet 584.7%SourceGemini 3.7 Flash—Not directly comparable
HLE w/ tools
Claude Sonnet 557.4%SourceGemini 3.7 Flash—Not directly comparable
OSWorld-Verified
Claude Sonnet 581.2%SourceGemini 3.7 Flash—Not directly comparable
Terminal-Bench 2.1 (Vals)
Claude Sonnet 574.5%SourceGemini 3.7 Flash77.5%SourceGemini 3.7 Flash leads this result
Terminal-Bench 2.1
Claude Sonnet 5—Gemini 3.7 Flash85.8%SourceNot directly comparable
AutomationBench
Claude Sonnet 5—Gemini 3.7 Flash30.4%SourceNot directly comparable
OSWorld 2.0
Claude Sonnet 5—Gemini 3.7 Flash47.9%SourceNot directly comparable
Agents' Last Exam
Claude Sonnet 5—Gemini 3.7 Flash26.3%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Sonnet 585.2%SourceGemini 3.7 Flash—Not directly comparable
SWE-bench Pro
Claude Sonnet 563.2%SourceGemini 3.7 Flash—Not directly comparable
SWE Multilingual
Claude Sonnet 578.3%SourceGemini 3.7 Flash—Not directly comparable
SWE Multimodal
Claude Sonnet 528.1%SourceGemini 3.7 Flash—Not directly comparable
Terminal-Bench 2.0
Claude Sonnet 580.4%SourceGemini 3.7 Flash—Not directly comparable
FrontierCode 1.1 Main
Claude Sonnet 542.7%SourceGemini 3.7 Flash43.6%SourceGemini 3.7 Flash leads this result
cursorBench32
Claude Sonnet 561.5%SourceGemini 3.7 Flash—Not directly comparable
VulcanBench CII v1
Claude Sonnet 589.2%SourceGemini 3.7 Flash—Not directly comparable
LiveCodeBench (Vals)
Claude Sonnet 582.4%SourceGemini 3.7 Flash88.7%SourceGemini 3.7 Flash leads this result
SWE-bench (Vals)
Claude Sonnet 579.6%SourceGemini 3.7 Flash80.8%SourceGemini 3.7 Flash leads this result
DeepSWE
Claude Sonnet 5—Gemini 3.7 Flash65.3%SourceNot directly comparable
Terminal-Bench 2.1
Claude Sonnet 5—Gemini 3.7 Flash85.8%SourceNot directly comparable
FrontierSWE v2
Claude Sonnet 5—Gemini 3.7 Flash20.3%SourceNot directly comparable
Reasoning
MRCR v2 64K-128K
Claude Sonnet 5—Gemini 3.7 Flash97%SourceNot directly comparable
Knowledge
HLE
Claude Sonnet 557.4%SourceGemini 3.7 Flash—Not directly comparable
HLE w/o tools
Claude Sonnet 543.2%SourceGemini 3.7 Flash—Not directly comparable
HLE-Verified
Shared sourceClaude Sonnet 531.0%Gemini 3.7 Flash53.6%Gemini 3.7 Flash leads this result
LABBench2
Shared sourceClaude Sonnet 580.1%Gemini 3.7 Flash82.1%Gemini 3.7 Flash leads this result
GPQA Diamond (Vals)
Claude Sonnet 588.9%SourceGemini 3.7 Flash93.9%SourceGemini 3.7 Flash leads this result
MMLU-Pro (Vals)
Claude Sonnet 587.5%SourceGemini 3.7 Flash90.1%SourceGemini 3.7 Flash leads this result
BioMysteryBench (human-solvable)
Claude Sonnet 5—Gemini 3.7 Flash87.1%SourceNot directly comparable
BioMysteryBench (human-difficult)
Claude Sonnet 5—Gemini 3.7 Flash43.5%SourceNot directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Sonnet 5 or Gemini 3.7 Flash?
Claude Sonnet 5 has the higher public score estimate, 69.84 versus 68.49, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Which is better for coding, Claude Sonnet 5 or Gemini 3.7 Flash?
Claude Sonnet 5 leads the public coding lane, 64 to 62.7, with Supported evidence for both models, although the 90% intervals overlap.
Which is better for agentic tasks, Claude Sonnet 5 or Gemini 3.7 Flash?
Claude Sonnet 5 leads the public agentic tasks lane, 65.8 to 64, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Claude Sonnet 5 or Gemini 3.7 Flash?
For the stated presets, chat costs $0.007 on Claude Sonnet 5 and $0.00263 on Gemini 3.7 Flash; repository review costs $0.13 and $0.04875; the cache-heavy agent loop costs $0.18 and $0.0675. Costs use the listed standard API rates.
Which has the larger context window, Claude Sonnet 5 or Gemini 3.7 Flash?
Both models list the same context window, 1M.
Related comparisons
Last updated September 10, 2026
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