Updated November 10, 2026. Rank says GPT-5.6 Luna is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-5.6 Luna has the higher public point estimate, 63.72 versus 29.7. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Mistral
29.7/100
Estimated · Public rank #179
Conditional range 15.3–44.0
GPT-5.6 Luna: $0.20 input / $1.20 output
Mistral Medium 3.5 128B: $1.50 input / $7.50 output
GPT-5.6 Luna: 1.05M
Mistral Medium 3.5 128B: 256K
GPT-5.6 Luna: July 9, 2026
Mistral Medium 3.5 128B: April 29, 2026
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.
Tool use, computer use, and multi-step task completion
GPT-5.6 Luna
GPT-5.6 Luna has the higher public agentic point estimate, 59.1 to 21.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-5.6 Luna
GPT-5.6 Luna has the larger documented context window.
1K fresh input + 500 output tokens
GPT-5.6 Luna
GPT-5.6 Luna 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
GPT-5.6 Luna
GPT-5.6 Luna 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.
50K fresh input + 3K output tokens
GPT-5.6 Luna
GPT-5.6 Luna has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
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.
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.
Directional only · BenchAlign v5.8
GPT-5.6 Luna 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.
The LLM Selector reads what you need a model for and returns a shortlist, ranked in the same order the job pages publish.
We keep what you type here, drafts included, for 180 days, and after that only the job and requirements read from it. Nothing is tied to your IP address or account. Leave out names and other personal details. Privacy
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
2 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.
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 10.7Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, reasoning, multimodal, 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 | GPT-5.6 Luna | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 59.1Supported · #33/132 | 21.3Supported · #115/132 | Like-for-likeBenchAlign v5.8 lane · 11 vs 3 public rows | GPT-5.6 Luna leads |
| Knowledge | 63.1Supported · #40/179 | 28.6Supported · #160/179 | Like-for-likeBenchAlign v5.8 lane · 6 vs 2 public rows | GPT-5.6 Luna leads |
| Coding | 61.9Supported · #17/158 | 21.6Estimated · #134/158 | Directional onlyBenchAlign v5.8 lane · 8 vs 2 public rows | Directional only |
| Reasoning | 58.3Supported · #36/158 | 23.8Estimated · #141/158 | Directional onlyBenchAlign v5.8 lane · 3 vs 0 public rows | Directional only |
| Multimodal | 65.4Supported · #23/67 | Not ranked | Not comparableBenchAlign v5.8 lane · 4 vs 0 public rows | Not comparable |
| Multilingual | Not ranked | Not ranked | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Instruction following | Not ranked | 84.0#55/157 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 78.9Unranked · 3 rankable rows | Not ranked | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
Ranks count the models scored in each category’s lane, so the agentic, coding, reasoning, multimodal, 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
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
GPT-5.6 Luna has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
GPT-5.6 Luna has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
Enter a month of tokens to price it at both models’ listed API rates.
Standard listed rates, no batch or volume discounts.
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.
GPT-5.6 Luna
1.05M
OpenAI model catalogMistral Medium 3.5 128B
256K
GPT-5.6 Luna
gpt-5.6-luna
OpenAI model catalogMistral Medium 3.5 128B
Not sourced
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
GPT-5.6 Luna
$0.02 per 1M cached input tokens
OpenAI pricingMistral Medium 3.5 128B
Not published
GPT-5.6 Luna
text, image
OpenAI model catalogMistral Medium 3.5 128B
text, image
Mistral-Medium-3.5-128B model cardGPT-5.6 Luna
Mistral Medium 3.5 128B
GPT-5.6 Luna
Generally Available · OpenAI Responses API
OpenAI model catalogMistral Medium 3.5 128B
Not sourced
GPT-5.6 Luna
Reasoning
Mistral Medium 3.5 128B
Reasoning
GPT-5.6 Luna
Proprietary
Mistral Medium 3.5 128B
Open Weight
GPT-5.6 Luna
Proprietary
Mistral Medium 3.5 128B
Open Weight
GPT-5.6 Luna
2026-07-09
Mistral Medium 3.5 128B
2026-04-29
GPT-5.6 Luna has the higher public point estimate, 63.72 versus 29.7. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.
GPT-5.6 Luna scores higher for coding on the public lane, 61.9 to 21.6. 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.
GPT-5.6 Luna has the higher public agentic tasks point estimate, 59.1 to 21.3, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.0008 on GPT-5.6 Luna and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.0136 and $0.0975; the cache-heavy agent loop costs $0.02 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
GPT-5.6 Luna has the larger documented context window: 1.05M, compared with 256K.
Estimates at 50,000 req/day · 1000 tokens/req average.
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Terminal-Bench 4.1
Not directly comparable
Terminal-Bench 3.0
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
BrowseComp
Not directly comparable
OSWorld 2.0
Not directly comparable
CyberGym
Not directly comparable
ExploitGym
Not directly comparable
Toolathlon
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-5.6 Luna leads this result
ApprenticeBench
Not directly comparable
MazeBench
Not directly comparable
τ³-bench results
Not directly comparable
Gert Labs
Not directly comparable
SWE-bench Pro
Not directly comparable
Terminal-Bench 2.1
Not directly comparable
DeepSWE
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
CursorBench 3.2
Not directly comparable
VulcanBench v3
Not directly comparable
SWE-bench (Vals)
GPT-5.6 Luna leads this result
CursorBench 4.0
Not directly comparable
SWE-bench Verified
Not directly comparable
ExtractBench (LlamaIndex)
Not directly comparable
ParseBench
Not directly comparable
MMMU-Pro
Not directly comparable
MMMU-Pro w/ Python
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Hard
Not directly comparable
GPQA Diamond (Vals)
GPT-5.6 Luna leads this result
MMLU-Pro (Vals)
GPT-5.6 Luna leads this result
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Last updated November 10, 2026
