Long documents
Prompts that approach the documented context limit
GPT-OSS 120B
GPT-OSS 120B has the larger documented context window.
Confidence: documented
GPT-OSS 120B vs Magistral Medium
Updated October 14, 2026. We do not rank this pair: at least one has no public score. 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
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Decision reading
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. 3 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
- Shared results
- 3
- GPT-OSS 120B only
- 5
- Magistral Medium only
- 3
- Like-for-like categories
- 0 / 8
Supported: GPT-OSS 120BHow 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.
Show secondary and unsupported calls
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
Magistral Medium is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limitedAgentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Magistral Medium is not ranked on the public lane for agentic, so no winner is named for agentic.
Confidence: limitedChat 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-ratesCache-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. GPT-OSS 120B does not fit this workload in one request. Magistral Medium does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate. Magistral Medium has no comparable published API token rate.
Confidence: listed-ratesRepository review cost
50K fresh input + 3K 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. Magistral Medium does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate. Magistral Medium has no comparable published API token rate.
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.
23.3GPT-OSS 120B—Magistral Medium
Not comparable · BenchAlign v5.8
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.
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.
5 GPT-OSS 120B only
3 Shared
3 Magistral Medium only
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.
GPQAKnowledge
Normalized gap 9.3GPT-OSS 120B:80.1%Magistral Medium:70.8%
Bars run 0–100 on each benchmark’s normalized display scale
Category results, on a stated basis
Each 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.
Agentic
Not comparable- GPT-OSS 120B
- 18.9
- Estimated · #106/124
- Magistral Medium
- Not ranked
- Basis
- BenchAlign v5.8 lane · 1 vs 0 public rows
- Reading
- Not comparable
Coding
Not comparable- GPT-OSS 120B
- 23.3
- Estimated · #110/149
- Magistral Medium
- Not ranked
- Basis
- BenchAlign v5.8 lane · 2 vs 2 public rows
- Reading
- Not comparable
Reasoning
Not comparable- GPT-OSS 120B
- 61.6
- Unranked · 2 rankable rows
- Magistral Medium
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- GPT-OSS 120B
- Not ranked
- Magistral Medium
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- GPT-OSS 120B
- 38.7
- Supported · #120/181
- Magistral Medium
- Not ranked
- Basis
- BenchAlign v5.8 lane · 3 vs 1 public rows
- Reading
- Not comparable
Multilingual
Not comparable- GPT-OSS 120B
- Not ranked
- Magistral Medium
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- GPT-OSS 120B
- 84.3
- #48/128
- Magistral Medium
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Math
Not comparable- GPT-OSS 120B
- Not ranked
- Magistral Medium
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
| Category | GPT-OSS 120B | Magistral Medium | Basis | Reading |
|---|---|---|---|---|
| Agentic | 18.9Estimated · #106/124 | Not ranked | Not comparableBenchAlign v5.8 lane · 1 vs 0 public rows | Not comparable |
| Coding | 23.3Estimated · #110/149 | Not ranked | Not comparableBenchAlign v5.8 lane · 2 vs 2 public rows | Not comparable |
| Reasoning | 61.6Unranked · 2 rankable rows | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Multimodal | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 38.7Supported · #120/181 | Not ranked | Not comparableBenchAlign v5.8 lane · 3 vs 1 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | 84.3#48/128 | 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 v5.8) 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.
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
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Magistral Medium
- API rate not published
- Fits in one request
GPT-OSS 120B has no comparable published API token rate. Magistral Medium has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Fits in one request
- Magistral Medium
- API rate not published
- Does not fit in one request
Magistral Medium does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate. Magistral Medium has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- GPT-OSS 120B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
- Magistral Medium
- API rate not published
- Does not fit in one request
- Cached-input rate unavailable
GPT-OSS 120B does not fit this workload in one request. Magistral Medium does not fit this workload in one request. GPT-OSS 120B has no comparable published API token rate. Magistral Medium has no comparable published API token 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.
SpecificationGPT-OSS 120BMagistral Medium
Context window
Maximum documented context; output-token limits may be lower.
GPT-OSS 120B
128K
Magistral Medium
40K
API model ID
GPT-OSS 120B
Not sourced
Magistral Medium
Not sourced
Cached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-OSS 120B
No comparable hosted API rate
Magistral Medium
No comparable hosted API rate
Documented inputs
GPT-OSS 120B
Not sourced
Magistral Medium
Not sourced
Documented outputs
GPT-OSS 120B
Not sourced
Magistral Medium
Not sourced
Provider availability
GPT-OSS 120B
Not sourced
Magistral Medium
Not sourced
Reasoning profile
GPT-OSS 120B
Non-Reasoning
Magistral Medium
Reasoning
Weight access
GPT-OSS 120B
Open Weight
Magistral Medium
Proprietary
License
GPT-OSS 120B
Open Weight
Magistral Medium
Proprietary
Release date
GPT-OSS 120B
2025-08-05
Magistral Medium
2025-06-10
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
- At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
- Workload cost
- A complete comparable API-rate estimate is not available for both models.
- Context tradeoff
- GPT-OSS 120B has the larger documented window (128K).
Run the same representative tasks against both endpoints before changing production traffic.
Questions
Which is better, GPT-OSS 120B or Magistral Medium?
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.
Which is better for coding, GPT-OSS 120B or Magistral Medium?
Magistral Medium is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, GPT-OSS 120B or Magistral Medium?
Magistral Medium is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, GPT-OSS 120B or Magistral Medium?
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, GPT-OSS 120B or Magistral Medium?
GPT-OSS 120B has the larger documented context window: 128K, compared with 40K.
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
Gert Labs
GPT-OSS 120B29.61%SourceMagistral Medium—Not directly comparable
Coding
SWE-bench Verified
GPT-OSS 120B62.4%SourceMagistral Medium—Not directly comparable
React Native Evals
GPT-OSS 120B71.6%SourceMagistral Medium—Not directly comparable
LiveCodeBench v5
GPT-OSS 120B—Magistral Medium59.4%SourceNot directly comparable
LiveCodeBench v6
GPT-OSS 120B—Magistral Medium50.3%SourceNot directly comparable
Knowledge
Math
11 public results · 3 shared
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Last updated October 14, 2026