Long documents
Prompts that approach the documented context limit
Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite has the larger documented context window.
Updated September 23, 2026. Rank cannot separate these two. Price, access, and your workload decide. 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
Claude Sonnet 4.5 has the higher public score estimate, 47.86 versus 47.46, but the 90% score intervals overlap. Treat that as a lead, not a settled winner. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
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.
Prompts that approach the documented context limit
Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite has the larger documented context window.
1K fresh input + 500 output tokens
Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.1 Flash-Lite
Gemini 3.1 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Claude Sonnet 4.5 is not ranked on the public lane for coding, so no winner is named for coding.
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Claude Sonnet 4.5 and Gemini 3.1 Flash-Lite are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
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 Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
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.
Not comparable · BenchAlign v5.6
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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Each row shows the public-lane category score for both models: the BenchAlign v5.6 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.
| Category | Claude Sonnet 4.5 | Gemini 3.1 Flash-Lite | Basis | Reading |
|---|---|---|---|---|
| Agentic | 26.7Estimated · #75/105 | 27.5Estimated · #73/105 | Directional onlyBenchAlign v5.6 lane · 5 vs 2 public rows | Directional only |
| Coding | Not ranked | 28.4Estimated · #100/135 | Not comparableBenchAlign v5.6 lane · 1 vs 3 public rows | Not comparable |
| Reasoning | 19.1Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 37.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 1 weighted rows | Not comparable |
| Knowledge | Not ranked | 44.0Estimated · #73/160 | Not comparableBenchAlign v5.6 lane · 1 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 0 weighted rows | Not comparable |
| Math | 34.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.6) 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.
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.
1K fresh input + 500 output tokens
Gemini 3.1 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Gemini 3.1 Flash-Lite has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
Maximum documented context; output-token limits may be lower.
Claude Sonnet 4.5
200K
Gemini 3.1 Flash-Lite
Claude Sonnet 4.5
Not sourced
Gemini 3.1 Flash-Lite
gemini-3.1-flash-lite
Google Gemini API pricingA missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Sonnet 4.5
Not published
Gemini 3.1 Flash-Lite
$0.025 per 1M cached input tokens
Google Gemini API pricingClaude Sonnet 4.5
Not sourced
Gemini 3.1 Flash-Lite
Not sourced
Claude Sonnet 4.5
Not sourced
Gemini 3.1 Flash-Lite
Not sourced
Claude Sonnet 4.5
Not sourced
Gemini 3.1 Flash-Lite
Not sourced
Claude Sonnet 4.5
Non-Reasoning
Gemini 3.1 Flash-Lite
Non-Reasoning
Claude Sonnet 4.5
Proprietary
Gemini 3.1 Flash-Lite
Proprietary
Claude Sonnet 4.5
Proprietary
Gemini 3.1 Flash-Lite
Proprietary
Claude Sonnet 4.5
2025-09-01
Gemini 3.1 Flash-Lite
2026-03-03
Claude Sonnet 4.5 has the higher public score estimate, 47.86 versus 47.46, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Claude Sonnet 4.5 is not ranked on the public lane for coding, so no winner is named for coding.
Gemini 3.1 Flash-Lite scores higher for agentic tasks on the public lane, 27.5 to 26.7. Claude Sonnet 4.5 and Gemini 3.1 Flash-Lite 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.
For the stated presets, chat costs $0.0105 on Claude Sonnet 4.5 and $0.001 on Gemini 3.1 Flash-Lite; repository review costs $0.195 and $0.017; the cache-heavy agent loop costs $0.81 and $0.025. Claude Sonnet 4.5 does not fit this workload in one request. Claude Sonnet 4.5 has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 3.1 Flash-Lite has the larger documented context window: 1M, compared with 200K.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 2.0
Not directly comparable
OSWorld-Verified
Not directly comparable
VITA-Bench
Not directly comparable
Gert Labs
Shared sourceClaude Sonnet 4.5 leads this result
JobBench
Not directly comparable
Terminal-Bench 2.1 (Vals)
Not directly comparable
SWE-bench Verified
Not directly comparable
Vibe Code Bench
Not directly comparable
LiveCodeBench (Vals)
Not directly comparable
SWE-bench (Vals)
Not directly comparable
ARC-AGI-2
Not directly comparable
CharXiv
Not directly comparable
One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.
Read a sample issueJoin 2,000+ readers.
Last updated September 23, 2026