Agentic
Directional only- K-Exaone
- 46.0
- Estimated · #86/154
- Mistral Medium 3.5 128B
- 22.6
- Supported · #152/154
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Directional only
K-Exaone vs Mistral Medium 3.5 128B
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.
Updated September 22, 2026. Rank says K-Exaone is ahead. Price, access, and your workload can each overturn that. 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
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.
No workload recommendation clears the current evidence threshold.
Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
K-Exaone and Mistral Medium 3.5 128B are scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
K-Exaone is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.
Confidence: limited
Long documents
Prompts that approach the documented context limit
No clear pick
The documented context windows are equal.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: rate-fallback
Repository review cost
50K fresh input + 3K output tokens
Not enough matched evidence
A complete comparable API-rate estimate is not available for both models.
Confidence: listed-rates
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.
45.2K-Exaone37.7Mistral Medium 3.5 128B
Directional only · BenchAlign
K-Exaone 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.
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
- Shared results
- 0
- K-Exaone only
- 0
- Mistral Medium 3.5 128B only
- 7
- Like-for-like categories
- 0 / 8
3 categories rest 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.
Coding
Directional only- K-Exaone
- 45.2
- Estimated · #93/156
- Mistral Medium 3.5 128B
- 37.7
- Estimated · #131/156
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Directional only
Knowledge
Directional only- K-Exaone
- 42.7
- Estimated · #124/186
- Mistral Medium 3.5 128B
- 38.9
- Supported · #144/186
- Basis
- BenchAlign lane · 0 vs 2 public rows
- Reading
- Directional only
Reasoning
Not comparable- K-Exaone
- 63.1
- Unranked · 2 rankable rows
- Mistral Medium 3.5 128B
- 68.6
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- K-Exaone
- Not ranked
- Mistral Medium 3.5 128B
- 55.7
- Unranked · 1 rankable row
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- K-Exaone
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- K-Exaone
- 77.2
- Unranked · 1 rankable row
- Mistral Medium 3.5 128B
- 82.6
- #48/124
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- K-Exaone
- Not ranked
- Mistral Medium 3.5 128B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | K-Exaone | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 46.0Estimated · #86/154 | 22.6Supported · #152/154 | Directional onlyBenchAlign lane · 0 vs 3 public rows | Directional only |
| Coding | 45.2Estimated · #93/156 | 37.7Estimated · #131/156 | Directional onlyBenchAlign lane · 0 vs 2 public rows | Directional only |
| Knowledge | 42.7Estimated · #124/186 | 38.9Supported · #144/186 | Directional onlyBenchAlign lane · 0 vs 2 public rows | Directional only |
| Reasoning | 63.1Unranked · 2 rankable rows | 68.6Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | 55.7Unranked · 1 rankable row | 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 | 77.2Unranked · 1 rankable row | 82.6#48/124 | 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) 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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
- K-Exaone
- API rate not published
- Fits in one request
- Mistral Medium 3.5 128B
- $0.00525
- Fits in one request
K-Exaone has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- K-Exaone
- API rate not published
- Fits in one request
- Mistral Medium 3.5 128B
- $0.0975
- Fits in one request
K-Exaone has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- K-Exaone
- API rate not published
- Fits in one request
- Cached-input rate unavailable
- Mistral Medium 3.5 128B
- $0.405
- Fits in one request
- Cached input priced at the published list-input rate
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate. K-Exaone has no comparable published API token rate.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationK-ExaoneMistral Medium 3.5 128B
Context window
Maximum documented context; output-token limits may be lower.
K-Exaone
256K
Mistral Medium 3.5 128B
256K
API model ID
K-Exaone
Not sourced
Mistral 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.
K-Exaone
No comparable hosted API rate
Mistral Medium 3.5 128B
Not published
Documented inputs
K-Exaone
Not sourced
Mistral Medium 3.5 128B
Not sourced
Documented outputs
K-Exaone
Not sourced
Mistral Medium 3.5 128B
Not sourced
Provider availability
K-Exaone
Not sourced
Mistral Medium 3.5 128B
Not sourced
Reasoning profile
K-Exaone
Reasoning
Mistral Medium 3.5 128B
Reasoning
Weight access
K-Exaone
Proprietary
Mistral Medium 3.5 128B
Open Weight
License
K-Exaone
Proprietary
Mistral Medium 3.5 128B
Open Weight
Release date
K-Exaone
Not sourced
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
- The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- Both models list 256K.
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 evidence7 rows
Agentic
Coding
Knowledge
Questions
Which is better, K-Exaone or Mistral Medium 3.5 128B?
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, K-Exaone or Mistral Medium 3.5 128B?
K-Exaone scores higher for coding on the public lane, 45.2 to 37.7. K-Exaone and Mistral Medium 3.5 128B 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, K-Exaone or Mistral Medium 3.5 128B?
K-Exaone scores higher for agentic tasks on the public lane, 46 to 22.6. K-Exaone is 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.
Which costs less, K-Exaone or Mistral Medium 3.5 128B?
Both models do not have comparable published API token rates, so this page does not name a universal price winner.
Which has the larger context window, K-Exaone or Mistral Medium 3.5 128B?
Both models list the same context window, 256K.
Related comparisons
Last updated September 22, 2026
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