Updated October 19, 2026. Rank says Gemini 3.5 Flash-Lite is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
Gemini 3.5 Flash-Lite has the higher public score estimate, 49.61 versus 48.19, 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.
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.
Code generation, repair, and software-engineering tasks
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the higher public coding point estimate, 35.8 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Tool use, computer use, and multi-step task completion
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the higher public agentic point estimate, 35.6 to 32.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the larger documented context window.
1K fresh input + 500 output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7
MiniMax M2.7 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
Like-for-like · BenchAlign v5.8
Gemini 3.5 Flash-Lite 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
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
Normalized gap 5.4SWE-bench ProCoding
Normalized gap 2.0LiveCodeBench (Vals)Coding
Normalized gap 0.9Each 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.
| Category | Gemini 3.5 Flash-Lite | MiniMax M2.7 | Basis | Reading |
|---|---|---|---|---|
| Agentic | 35.6Supported · #81/130 | 32.4Supported · #92/130 | Like-for-likeBenchAlign v5.8 lane · 3 vs 8 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Coding | 35.8Supported · #77/151 | 34.4Supported · #81/151 | Like-for-likeBenchAlign v5.8 lane · 4 vs 12 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Knowledge | 49.1Supported · #86/186 | 46.1Supported · #95/186 | Like-for-likeBenchAlign v5.8 lane · 2 vs 5 public rows | Gemini 3.5 Flash-Lite leads · intervals overlap |
| Reasoning | 70.9Unranked · 3 rankable rows | 78.8Unranked · 2 rankable rows | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multimodal | 80.9Unranked · 1 rankable row | 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 | 93.0#13/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 37.8Unranked · 2 rankable rows | 70.5Unranked · 4 rankable rows | Not comparableProvisional lane · 2 vs 2 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.
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
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
MiniMax M2.7 has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 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.
Gemini 3.5 Flash-Lite
MiniMax M2.7
200K
Gemini 3.5 Flash-Lite
gemini-3.5-flash-lite
Google Gemini 3.5 Flash-Lite model documentationMiniMax M2.7
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.5 Flash-Lite
$0.03 per 1M cached input tokens
Google Gemini API pricingMiniMax M2.7
Not published
Gemini 3.5 Flash-Lite
text, image, video, audio, pdf
Google Gemini 3.5 Flash-Lite model documentationMiniMax M2.7
Not sourced
Gemini 3.5 Flash-Lite
MiniMax M2.7
Not sourced
Gemini 3.5 Flash-Lite
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMiniMax M2.7
Not sourced
Gemini 3.5 Flash-Lite
Reasoning
MiniMax M2.7
Non-Reasoning
Gemini 3.5 Flash-Lite
Proprietary
MiniMax M2.7
Open Weight
Gemini 3.5 Flash-Lite
Proprietary
MiniMax M2.7
Open Weight
Gemini 3.5 Flash-Lite
2026-07-21
MiniMax M2.7
2026-03-18
Gemini 3.5 Flash-Lite has the higher public score estimate, 49.61 versus 48.19, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.
Gemini 3.5 Flash-Lite has the higher public coding point estimate, 35.8 to 34.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Gemini 3.5 Flash-Lite has the higher public agentic tasks point estimate, 35.6 to 32.4, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.0009 on MiniMax M2.7; repository review costs $0.0225 and $0.0186; the cache-heavy agent loop costs $0.037 and $0.078. MiniMax M2.7 does not fit this workload in one request. MiniMax M2.7 has no published cached-input rate, so cached tokens use its listed input rate.
Gemini 3.5 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 2.1
Not directly comparable
OSWorld-Verified
Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.5 Flash-Lite leads this result
Terminal-Bench 2.0
Not directly comparable
τ³-bench results
Not directly comparable
Toolathlon
Not directly comparable
MLE-Bench Lite
Not directly comparable
MM-ClawBench
Not directly comparable
Claw-Eval
Not directly comparable
Gert Labs
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
SWE-bench Pro
MiniMax M2.7 leads this result
LiveCodeBench (Vals)
MiniMax M2.7 leads this result
SWE-bench (Vals)
Gemini 3.5 Flash-Lite leads this result
SWE-bench Verified*
Not directly comparable
SWE-Rebench
Not directly comparable
SWE Multilingual
Not directly comparable
Multi-SWE Bench
Not directly comparable
VIBE-Pro
Not directly comparable
NL2Repo
Not directly comparable
Terminal-Bench 2.0
Not directly comparable
Vibe Code Bench
Not directly comparable
React Native Evals
Not directly comparable
MRCRv2
Not directly comparable
GPQA Diamond (Vals)
MiniMax M2.7 leads this result
MMLU-Pro (Vals)
Gemini 3.5 Flash-Lite leads this result
HLE
Not directly comparable
GPQA-D
Not directly comparable
MMLU-Pro (Arcee)
Not directly comparable
FrontierMath v2 (Tiers 1-3)
Not directly comparable
FrontierMath v2 (Tier 4)
Not directly comparable
AIME25 (Arcee)
Not directly comparable
AIME26
Not directly comparable
HMMT Nov 2025
Not directly comparable
HMMT Feb 2026
Not directly comparable
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Last updated October 19, 2026
