Updated October 17, 2026. Rank says GPT-6 Astra is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.
GPT-6 Astra has the higher public point estimate, 83.88 versus 33.62. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.
Mistral
33.62/100
Estimated · Public rank #162
Conditional range 19.3–48.0
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-6 Astra
GPT-6 Astra has the higher public agentic point estimate, 74.8 to 19.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
Prompts that approach the documented context limit
GPT-6 Astra
GPT-6 Astra has the larger documented context window.
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.
200K cached + 20K fresh input + 10K output tokens
Mistral Medium 3.5 128B
Mistral Medium 3.5 128B 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
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.
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-6 Astra 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.
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
1 category rests 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.
A shared-evidence shape is not available.
BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.
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.
| Category | GPT-6 Astra | Mistral Medium 3.5 128B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 74.8Supported · #7/128 | 19.8Supported · #108/128 | Like-for-likeBenchAlign v5.8 lane · 14 vs 3 public rows | GPT-6 Astra leads |
| Knowledge | 86.2Supported · #3/184 | 31.8Supported · #148/184 | Like-for-likeBenchAlign v5.8 lane · 7 vs 2 public rows | GPT-6 Astra leads |
| Coding | 75.5Supported · #4/150 | 22.6Estimated · #114/150 | Directional onlyBenchAlign v5.8 lane · 6 vs 2 public rows | Directional only |
| Reasoning | 93.3#1/28 | 73.5Unranked · 2 rankable rows | Not comparableProvisional lane · 2 vs 0 weighted rows | Not comparable |
| Multimodal | 87.3Unranked · 3 rankable rows | 60.1Unranked · 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 | Not ranked | 84.0#52/148 | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Math | 97.3Unranked · 2 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, 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
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
50K fresh input + 3K output tokens
Mistral Medium 3.5 128B has the lower modeled cost
Costs use the listed standard API rates.
200K cached + 20K fresh input + 10K output tokens
Mistral Medium 3.5 128B has the lower modeled cost
Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
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-6 Astra
Mistral Medium 3.5 128B
256K
GPT-6 Astra
gpt-6-astra
OpenAI GPT-6 Astra model documentationMistral 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-6 Astra
$1 per 1M cached input tokens
OpenAI GPT-6 Astra model documentationMistral Medium 3.5 128B
Not published
GPT-6 Astra
text, image
OpenAI GPT-6 Astra model documentationMistral Medium 3.5 128B
Not sourced
GPT-6 Astra
Mistral Medium 3.5 128B
Not sourced
GPT-6 Astra
Limited Availability · OpenAI Responses API, OpenAI Chat Completions API, ChatGPT Plus, Pro, Business, and Enterprise, Microsoft Azure, AWS Bedrock
OpenAI GPT-6 Astra model documentationMistral Medium 3.5 128B
Not sourced
GPT-6 Astra
Reasoning
Mistral Medium 3.5 128B
Reasoning
GPT-6 Astra
Proprietary
Mistral Medium 3.5 128B
Open Weight
GPT-6 Astra
Proprietary
Mistral Medium 3.5 128B
Open Weight
GPT-6 Astra
2026-09-03
Mistral Medium 3.5 128B
2026-04-29
GPT-6 Astra has the higher public point estimate, 83.88 versus 33.62. 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-6 Astra scores higher for coding on the public lane, 75.5 to 22.6. 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-6 Astra has the higher public agentic tasks point estimate, 74.8 to 19.8, with Supported evidence for both models. The conditional ranges do not establish rank confidence.
For the stated presets, chat costs $0.035 on GPT-6 Astra and $0.00525 on Mistral Medium 3.5 128B; repository review costs $0.65 and $0.0975; the cache-heavy agent loop costs $0.9 and $0.405. Mistral Medium 3.5 128B has no published cached-input rate, so cached tokens use its listed input rate.
GPT-6 Astra 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.
BrowseComp
Not directly comparable
OSWorld 2.0
Not directly comparable
Terminal-Bench 4.0
Not directly comparable
Terminal-Bench-Science 0.1
Not directly comparable
ExploitGym
Not directly comparable
Agents' Last Exam
Not directly comparable
AutomationBench
Not directly comparable
HLE w/ tools
Not directly comparable
Terminal-Bench 2.1 (Vals)
GPT-6 Astra leads this result
ApprenticeBench
Not directly comparable
CWE-bench v1
Not directly comparable
Hermes Index
Not directly comparable
Hermes Bench
Not directly comparable
MazeBench
Not directly comparable
τ³-bench results
Not directly comparable
Gert Labs
Not directly comparable
DeepSWE
Not directly comparable
FrontierCode 1.1 Main
Not directly comparable
FrontierCode 1.1 Extended
Not directly comparable
FrontierSWE v2
Not directly comparable
PostTrainBench v1.1
Not directly comparable
Bug Hunt Bench
Not directly comparable
SWE-bench Verified
Not directly comparable
SWE-bench (Vals)
Not directly comparable
STEMBench
Not directly comparable
ARC-AGI-1
Not directly comparable
ARC-AGI-2
Not directly comparable
ARC-AGI-3
Not directly comparable
GeneBench-Pro
Not directly comparable
MRCR v2 256K-512K
Not directly comparable
MRCR v2 512K-1M
Not directly comparable
GraphWalks BFS 256K–1M
Not directly comparable
GPQA
Not directly comparable
GPQA-D
Not directly comparable
HealthBench (raw)
Not directly comparable
HealthBench (length-adjusted)
Not directly comparable
HealthBench Professional
Not directly comparable
HealthBench Professional (raw)
Not directly comparable
HealthBench Hard
Not directly comparable
GPQA Diamond (Vals)
Not directly comparable
MMLU-Pro (Vals)
Not directly comparable
Gray Swan IPI (15 attempts)
Not directly comparable
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Last updated October 17, 2026
