Agentic
Like-for-like- Gemini 3.6 Flash
- 50.7
- Supported · #60/151
- Mistral Medium 3.5 128B
- 27.5
- Supported · #141/151
- Basis
- BenchAlign lane · 2 vs 3 public rows
- Reading
- Gemini 3.6 Flash leads · intervals overlap
Gemini 3.6 Flash vs Mistral Medium 3.5 128B
Updated September 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Gemini 3.6 Flash has the higher public score, 70.11 versus 48.95, and the 90% score intervals do not overlap.
4 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.
Agentic work
Tool use, computer use, and multi-step task completion
Gemini 3.6 Flash
Gemini 3.6 Flash leads on the public agentic lane, 50.7 to 27.5, with Supported evidence for both models, although the 90% intervals overlap.
Confidence: limited
Long documents
Prompts that approach the documented context limit
Gemini 3.6 Flash
Gemini 3.6 Flash has the larger documented context window.
Confidence: documented
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.6 Flash
Gemini 3.6 Flash has the lower estimated token cost for this stated workload. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
No clear pick
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Repository review cost
50K fresh input + 3K output tokens
No clear pick
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.
4 Gemini 3.6 Flash only
4 Shared
3 Mistral Medium 3.5 128B only
- Shared results
- 4
- Gemini 3.6 Flash only
- 4
- Mistral Medium 3.5 128B only
- 3
- 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 3.6 Flash
- 68.6
- Supported · #18/181
- Mistral Medium 3.5 128B
- 42.0
- Supported · #131/181
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- Gemini 3.6 Flash leads
Coding
Directional only- Gemini 3.6 Flash
- 58.9
- Supported · #30/183
- Mistral Medium 3.5 128B
- 41.1
- Estimated · #136/183
- Basis
- BenchAlign lane · 4 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- Gemini 3.6 Flash
- 77.8
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- 68.0
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Gemini 3.6 Flash
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- Gemini 3.6 Flash
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Gemini 3.6 Flash
- 82.3
- Unranked · 1 rankable row
- Mistral Medium 3.5 128B
- 55.4
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Gemini 3.6 Flash
- Not ranked
- Mistral Medium 3.5 128B
- 83.7
- #47/120
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Gemini 3.6 Flash | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 50.7Supported · #60/151 | 27.5Supported · #141/151 | Like-for-likeBenchAlign lane · 2 vs 3 public rows | Gemini 3.6 Flash leads · intervals overlap |
| Knowledge | 68.6Supported · #18/181 | 42.0Supported · #131/181 | Like-for-likeBenchAlign lane · 2 vs 2 public rows | Gemini 3.6 Flash leads |
| Coding | 58.9Supported · #30/183 | 41.1Estimated · #136/183 | Directional onlyBenchAlign lane · 4 vs 2 public rows | Directional only |
| Reasoning | 77.8Unranked · 2 rankable rows | 68.0Unranked · 2 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 |
| Multimodal | 82.3Unranked · 1 rankable row | 55.4Unranked · 1 rankable row | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 83.7#47/120 | 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.
MMLU-Pro (Vals)
Knowledge
Gemini 3.6 Flash: 89.3%Mistral Medium 3.5 128B: 75.3%Normalized gap 14.0Gemini 3.6 Flash source Mistral Medium 3.5 128B 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 3.6 Flash
- $0.00525
- Fits in one request
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Gemini 3.6 Flash
- $0.0975
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
Modeled costs are equal
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Gemini 3.6 Flash
- $0.135
- Fits in one request
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
Gemini 3.6 Flash has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationGemini 3.6 FlashMistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
Gemini 3.6 Flash
Mistral Medium 3.5 128B
256K
API model ID
Gemini 3.6 Flash
gemini-3.6-flash
Google Gemini 3.6 Flash model documentationMistral Medium 3.5 128B
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Gemini 3.6 Flash
$0.15 per 1M cached input tokens
Google Gemini API pricingMistral Medium 3.5 128B
Not published
Documented inputs
Gemini 3.6 Flash
text, image, video, audio, pdf
Google Gemini 3.6 Flash model documentationMistral Medium 3.5 128B
Not sourced
Documented outputs
Gemini 3.6 Flash
Mistral Medium 3.5 128B
Not sourced
Provider availability
Gemini 3.6 Flash
Generally Available · Gemini API, Google AI Studio
Google latest Gemini model guideMistral Medium 3.5 128B
Not sourced
Reasoning profile
Gemini 3.6 Flash
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
Gemini 3.6 Flash
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
Gemini 3.6 Flash
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
Gemini 3.6 Flash
2026-07-21
Mistral Medium 3.5 128B
2026-04-29
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 3.6 Flash has the higher public score, 70.11 versus 48.95, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.0975 vs $0.0975. Cache-heavy agent loop: $0.135 vs $0.405.
- Context tradeoff
- Gemini 3.6 Flash 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 evidence11 rows
Agentic
OSWorld-Verified
Gemini 3.6 Flash83%SourceMistral Medium 3.5 128B—Not directly comparable
Terminal-Bench 2.1 (Vals)
Gemini 3.6 Flash73.8%SourceMistral Medium 3.5 128B39.0%SourceGemini 3.6 Flash leads this result
τ³-bench results
Gemini 3.6 Flash—Mistral Medium 3.5 128B91.4%SourceNot directly comparable
Gert Labs
Gemini 3.6 Flash—Mistral Medium 3.5 128B39.10%SourceNot directly comparable
Coding
deepSwe
Gemini 3.6 Flash49%SourceMistral Medium 3.5 128B—Not directly comparable
cursorBench32
Gemini 3.6 Flash53.5%SourceMistral Medium 3.5 128B—Not directly comparable
LiveCodeBench (Vals)
Gemini 3.6 Flash88.1%SourceMistral Medium 3.5 128B—Not directly comparable
SWE-bench (Vals)
Gemini 3.6 Flash79.6%SourceMistral Medium 3.5 128B66.4%SourceGemini 3.6 Flash leads this result
SWE-bench Verified
Gemini 3.6 Flash—Mistral Medium 3.5 128B77.6%SourceNot directly comparable
Knowledge
Frequently asked questions
Which is better, Gemini 3.6 Flash or Mistral Medium 3.5 128B?
Gemini 3.6 Flash has the higher public score, 70.11 versus 48.95, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Gemini 3.6 Flash or Mistral Medium 3.5 128B?
Gemini 3.6 Flash scores higher for coding on the public lane, 58.9 to 41.1. Mistral Medium 3.5 128B 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 3.6 Flash or Mistral Medium 3.5 128B?
Gemini 3.6 Flash leads the public agentic tasks lane, 50.7 to 27.5, with Supported evidence for both models, although the 90% intervals overlap.
Which costs less, Gemini 3.6 Flash or Mistral Medium 3.5 128B?
For the stated presets, chat costs $0.00525 on Gemini 3.6 Flash and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0975 and $0.0975; the cache-heavy agent loop costs $0.135 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Gemini 3.6 Flash or Mistral Medium 3.5 128B?
Gemini 3.6 Flash has the larger documented context window: 1M, compared with 256K.
Related comparisons
Last updated September 4, 2026
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