Long documents
Prompts that approach the documented context limit
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the larger documented context window.
Confidence: documented
Mistral Medium 3.5 128B vs o1-preview
Updated September 29, 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
Share or export
Decision reading
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead. 0 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 0
- Mistral Medium 3.5 128B only
- 7
- o1-preview only
- 0
- Like-for-like categories
- 0 / 8
Estimated: Mistral Medium 3.5 128B · Supported: o1-previewHow the comparison works
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.
Chat turn cost
1K fresh input + 500 output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B 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
Repository review cost
50K fresh input + 3K output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-ratesCoding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Mistral Medium 3.5 128B and o1-preview are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
O1-preview is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedCache-heavy agent loop cost
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. o1-preview does not fit this workload in one request. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. o1-preview has no published cached-input rate, so cached tokens use its listed input rate.
Confidence: rate-fallback
Which one for a specific job
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.
26.6Mistral Medium 3.5 128B23.6o1-preview
Directional only · BenchAlign v5.7
Mistral Medium 3.5 128B scores higher, but at least one score rests on Estimated evidence or a different benchmark set. Directional only, no winner.
Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.
Same basis rules as the category table below
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
7 Mistral Medium 3.5 128B only
1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
Category results, on a stated basis
Each row shows the public-lane category score for both models: the BenchAlign v5.7 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
Directional only- Mistral Medium 3.5 128B
- 26.6
- Estimated · #102/143
- o1-preview
- 23.6
- Estimated · #110/143
- Basis
- BenchAlign v5.7 lane · 2 vs 0 public rows
- Reading
- Directional only
Agentic
Not comparable- Mistral Medium 3.5 128B
- 19.7
- Supported · #100/117
- o1-preview
- Not ranked
- Basis
- BenchAlign v5.7 lane · 3 vs 0 public rows
- Reading
- Not comparable
Reasoning
Not comparable- Mistral Medium 3.5 128B
- 69.9
- Unranked · 2 rankable rows
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- Mistral Medium 3.5 128B
- 56.7
- Unranked · 1 rankable row
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- Mistral Medium 3.5 128B
- 33.6
- Supported · #121/169
- o1-preview
- Not ranked
- Basis
- BenchAlign v5.7 lane · 2 vs 0 public rows
- Reading
- Not comparable
Multilingual
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- Mistral Medium 3.5 128B
- 82.6
- #48/124
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- Mistral Medium 3.5 128B
- Not ranked
- o1-preview
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | Mistral Medium 3.5 128B | o1-preview | Basis | Reading |
|---|---|---|---|---|
| Coding | 26.6Estimated · #102/143 | 23.6Estimated · #110/143 | Directional onlyBenchAlign v5.7 lane · 2 vs 0 public rows | Directional only |
| Agentic | 19.7Supported · #100/117 | Not ranked | Not comparableBenchAlign v5.7 lane · 3 vs 0 public rows | Not comparable |
| Reasoning | 69.9Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | 56.7Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 33.6Supported · #121/169 | Not ranked | Not comparableBenchAlign v5.7 lane · 2 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 82.6#48/124 | Not ranked | 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 |
Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.7) 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.
Supported evidence per lane · bars run 0–100Methodology
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
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
- o1-preview
- $0.045
- Fits in one request
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
- o1-preview
- $0.93
- Fits in one request
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
- o1-preview
- $3.90
- Does not fit in one request
- Cached input priced at the published list-input rate
o1-preview does not fit this workload in one request. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. o1-preview has no published cached-input rate, so cached tokens use its listed input rate.
Cached input falls back to the list input rate only where a cached rate is unpublished
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationMistral Medium 3.5 128Bo1-preview
Context window
Maximum documented context; output-token limits may be lower.
Mistral Medium 3.5 128B
256K
o1-preview
200K
API model ID
Mistral Medium 3.5 128B
Not sourced
o1-preview
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Mistral Medium 3.5 128B
Not published
o1-preview
Not published
Documented inputs
Mistral Medium 3.5 128B
Not sourced
o1-preview
Not sourced
Documented outputs
Mistral Medium 3.5 128B
Not sourced
o1-preview
Not sourced
Provider availability
Mistral Medium 3.5 128B
Not sourced
o1-preview
Not sourced
Reasoning profile
Mistral Medium 3.5 128B
Reasoning
o1-preview
Reasoning
Weight access
Mistral Medium 3.5 128B
Open Weight
o1-preview
Proprietary
License
Mistral Medium 3.5 128B
Open Weight
o1-preview
Proprietary
Release date
Mistral Medium 3.5 128B
2026-04-29
o1-preview
2024-09-12
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- Repository review: $0.0975 vs $0.93. Cache-heavy agent loop: $0.405 vs $3.90.
- Context tradeoff
- Mistral Medium 3.5 128B has the larger documented window (256K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, Mistral Medium 3.5 128B or o1-preview?
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, Mistral Medium 3.5 128B or o1-preview?
Mistral Medium 3.5 128B scores higher for coding on the public lane, 26.6 to 23.6. Mistral Medium 3.5 128B and o1-preview are 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, Mistral Medium 3.5 128B or o1-preview?
O1-preview is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, Mistral Medium 3.5 128B or o1-preview?
For the stated presets, chat costs $0.00525 on Mistral Medium 3.5 128B and $0.045 on o1-preview; repository review costs $0.0975 and $0.93; the cache-heavy agent loop costs $0.405 and $3.90. o1-preview does not fit this workload in one request. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. o1-preview has no published cached-input rate, so cached tokens use its listed input rate.
Which has the larger context window, Mistral Medium 3.5 128B or o1-preview?
Mistral Medium 3.5 128B has the larger documented context window: 256K, compared with 200K.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence7 rows
Agentic
Coding
Knowledge
7 public results · 0 shared
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Last updated September 29, 2026