Coding
Like-for-like- Claude Mythos 5
- 89.7
- DeepSeek V4 Pro 0813
- 70.9
- Weighted basis
- 2 vs 2 rows
- Reading
- Claude Mythos 5 leads
Claude Mythos 5 vs DeepSeek V4 Pro 0813
Updated August 13, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
Decision reading
Claude Mythos 5 has the higher public score, 82.87 versus 60.93, and the 90% score intervals do not overlap.
7 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.
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.
Coding work
Code generation, repair, and software-engineering tasks
Claude Mythos 5
Claude Mythos 5 leads on the same 2 weighted benchmark rows.
Confidence: limited
Chat turn cost
1K fresh input + 500 output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 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
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are 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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
8 Claude Mythos 5 only
7 Shared
27 DeepSeek V4 Pro 0813 only
- Shared results
- 7
- Claude Mythos 5 only
- 8
- DeepSeek V4 Pro 0813 only
- 27
- Like-for-like categories
- 1 / 8
2 categories use different evidence sets. Those rows remain visible for coverage context but do not name a winner.
Category results, on a stated basis
Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.
Agentic
Directional only- Claude Mythos 5
- 87.0
- DeepSeek V4 Pro 0813
- 74.5
- Weighted basis
- 3 vs 2 rows
- Reading
- Directional only
Knowledge
Directional only- Claude Mythos 5
- 68.5
- DeepSeek V4 Pro 0813
- 62.5
- Weighted basis
- 2 vs 4 rows
- Reading
- Directional only
Reasoning
Not comparable- Claude Mythos 5
- Not measured
- DeepSeek V4 Pro 0813
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Math
Not comparable- Claude Mythos 5
- 97.6
- DeepSeek V4 Pro 0813
- 95.2
- Weighted basis
- 1 vs 1 rows
- Reading
- Not comparable
Multilingual
Not comparable- Claude Mythos 5
- Not measured
- DeepSeek V4 Pro 0813
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
Multimodal
Not comparable- Claude Mythos 5
- 93.5
- DeepSeek V4 Pro 0813
- Not measured
- Weighted basis
- 1 vs 0 rows
- Reading
- Not comparable
Instruction following
Not comparable- Claude Mythos 5
- Not measured
- DeepSeek V4 Pro 0813
- Not measured
- Weighted basis
- 0 vs 0 rows
- Reading
- Not comparable
| Category | Claude Mythos 5 | DeepSeek V4 Pro 0813 | Weighted basis | Reading |
|---|---|---|---|---|
| Coding | 89.7 | 70.9 | Like-for-like2 vs 2 rows | Claude Mythos 5 leads |
| Agentic | 87.0 | 74.5 | Directional only3 vs 2 rows | Directional only |
| Knowledge | 68.5 | 62.5 | Directional only2 vs 4 rows | Directional only |
| Reasoning | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Math | 97.6 | 95.2 | Not comparable1 vs 1 rows | Not comparable |
| Multilingual | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
| Multimodal | 93.5 | Not measured | Not comparable1 vs 0 rows | Not comparable |
| Instruction following | Not measured | Not measured | Not comparable0 vs 0 rows | Not comparable |
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.
SWE-bench Pro
Coding
Claude Mythos 5: 80.3%DeepSeek V4 Pro 0813: 55.4%Normalized gap 24.9Claude Mythos 5 source DeepSeek V4 Pro 0813 sourceHLE
Knowledge
Claude Mythos 5: 64.5%DeepSeek V4 Pro 0813: 42.7%Normalized gap 21.8Claude Mythos 5 source DeepSeek V4 Pro 0813 sourceTerminal-Bench 2.0
Agentic
Claude Mythos 5: 88%DeepSeek V4 Pro 0813: 67.9%Normalized gap 20.1Claude Mythos 5 source DeepSeek V4 Pro 0813 sourceSWE-bench Verified
Coding
Claude Mythos 5: 95.5%DeepSeek V4 Pro 0813: 80.6%Normalized gap 14.9Claude Mythos 5 source DeepSeek V4 Pro 0813 sourceBrowseComp
Agentic
Claude Mythos 5: 88%DeepSeek V4 Pro 0813: 83.4%Normalized gap 4.6Claude Mythos 5 source DeepSeek V4 Pro 0813 source
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
- Claude Mythos 5
- $0.035
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.00087
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Repository review
50K fresh input + 3K output tokens
- Claude Mythos 5
- $0.65
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.02436
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- Claude Mythos 5
- $0.9
- Fits in one request
- DeepSeek V4 Pro 0813
- $0.01812
- Fits in one request
DeepSeek V4 Pro 0813 has the lower modeled cost
Costs use the listed standard API rates.
Specification differences
Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.
SpecificationClaude Mythos 5DeepSeek V4 Pro 0813
Context window
Maximum documented context; output-token limits may be lower.
Claude Mythos 5
DeepSeek V4 Pro 0813
API model ID
Claude Mythos 5
claude-mythos-5
Anthropic model overviewDeepSeek V4 Pro 0813
deepseek-v4-pro
DeepSeek models and pricingCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
Claude Mythos 5
$1 per 1M cached input tokens
Claude API pricingDeepSeek V4 Pro 0813
$0.003625 per 1M cached input tokens
Documented inputs
Claude Mythos 5
text, image
Anthropic model overviewDeepSeek V4 Pro 0813
Documented outputs
Claude Mythos 5
DeepSeek V4 Pro 0813
Provider availability
Claude Mythos 5
Limited Access · Claude API
Anthropic model overviewDeepSeek V4 Pro 0813
Generally Available · DeepSeek API
DeepSeek models and pricingReasoning profile
Claude Mythos 5
Reasoning
DeepSeek V4 Pro 0813
Reasoning
Weight access
Claude Mythos 5
Proprietary
DeepSeek V4 Pro 0813
Proprietary
License
Claude Mythos 5
Proprietary
DeepSeek V4 Pro 0813
Proprietary
Release date
Claude Mythos 5
2026-06-09
DeepSeek V4 Pro 0813
2026-08-13
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
- Claude Mythos 5 has the higher public score, 82.87 versus 60.93, and the 90% score intervals do not overlap.
- Workload cost
- Repository review: $0.65 vs $0.02436. Cache-heavy agent loop: $0.9 vs $0.01812.
- Context tradeoff
- Both models list 1M.
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 evidence42 rows
Agentic
Terminal-Bench 2.0
Claude Mythos 588%SourceDeepSeek V4 Pro 081367.9%SourceClaude Mythos 5 leads this result
OSWorld-Verified
Claude Mythos 585%SourceDeepSeek V4 Pro 0813—Not directly comparable
BrowseComp
Claude Mythos 588%SourceDeepSeek V4 Pro 081383.4%SourceClaude Mythos 5 leads this result
ExploitGym
Claude Mythos 517.5%SourceDeepSeek V4 Pro 0813—Not directly comparable
Terminal-Bench 2.1
Claude Mythos 5—DeepSeek V4 Pro 081387.9%SourceNot directly comparable
HLE w/ tools
Claude Mythos 5—DeepSeek V4 Pro 081360.0%SourceNot directly comparable
MCP Atlas
Claude Mythos 5—DeepSeek V4 Pro 081373.6%SourceNot directly comparable
Toolathlon
Claude Mythos 5—DeepSeek V4 Pro 081351.8%SourceNot directly comparable
CyberGym
Claude Mythos 5—DeepSeek V4 Pro 081383.3%SourceNot directly comparable
Toolathlon-Verified
Claude Mythos 5—DeepSeek V4 Pro 081374.1%SourceNot directly comparable
Agents' Last Exam
Claude Mythos 5—DeepSeek V4 Pro 081325.7%SourceNot directly comparable
AutomationBench
Claude Mythos 5—DeepSeek V4 Pro 081331.8%SourceNot directly comparable
Coding
SWE-bench Verified
Claude Mythos 595.5%SourceDeepSeek V4 Pro 081380.6%SourceClaude Mythos 5 leads this result
SWE-bench Pro
Claude Mythos 580.3%SourceDeepSeek V4 Pro 081355.4%SourceClaude Mythos 5 leads this result
Terminal-Bench 2.0
Claude Mythos 588.0%SourceDeepSeek V4 Pro 081367.9%SourceClaude Mythos 5 leads this result
LiveCodeBench Pass@1-COT
Claude Mythos 5—DeepSeek V4 Pro 081393.5%SourceNot directly comparable
Codeforces
Claude Mythos 5—DeepSeek V4 Pro 08133206.0SourceNot directly comparable
SWE Multilingual
Claude Mythos 5—DeepSeek V4 Pro 081376.2%SourceNot directly comparable
Vibe Code Bench
Claude Mythos 5—DeepSeek V4 Pro 081349.93%SourceNot directly comparable
Terminal-Bench 2.1
Claude Mythos 5—DeepSeek V4 Pro 081387.9%SourceNot directly comparable
NL2Repo
Claude Mythos 5—DeepSeek V4 Pro 081361.5%SourceNot directly comparable
deepSwe
Claude Mythos 5—DeepSeek V4 Pro 081362.7%SourceNot directly comparable
DSBench-FullStack
Claude Mythos 5—DeepSeek V4 Pro 081371.1%SourceNot directly comparable
DSBench-Hard
Claude Mythos 5—DeepSeek V4 Pro 081367.2%SourceNot directly comparable
Reasoning
Knowledge
GPQA
Claude Mythos 594.1%SourceDeepSeek V4 Pro 081390.1%SourceClaude Mythos 5 leads this result
HLE
Claude Mythos 564.5%SourceDeepSeek V4 Pro 081342.7%SourceClaude Mythos 5 leads this result
HLE w/o tools
Claude Mythos 559%SourceDeepSeek V4 Pro 0813—Not directly comparable
MMLU-Pro
Claude Mythos 5—DeepSeek V4 Pro 081387.5%SourceNot directly comparable
SimpleQA
Claude Mythos 5—DeepSeek V4 Pro 081357.9%SourceNot directly comparable
Chinese-SimpleQA
Claude Mythos 5—DeepSeek V4 Pro 081384.4%SourceNot directly comparable
GPQA-D
Claude Mythos 5—DeepSeek V4 Pro 081390.1%SourceNot directly comparable
Math
USAMO 2026
Claude Mythos 597.6%SourceDeepSeek V4 Pro 0813—Not directly comparable
HMMT Feb 2026
Claude Mythos 5—DeepSeek V4 Pro 081395.2%SourceNot directly comparable
IMOAnswerBench
Claude Mythos 5—DeepSeek V4 Pro 081389.8%SourceNot directly comparable
Apex
Claude Mythos 5—DeepSeek V4 Pro 081338.3%SourceNot directly comparable
Apex Shortlist
Claude Mythos 5—DeepSeek V4 Pro 081390.2%SourceNot directly comparable
Multilingual
SWE Multilingual
Claude Mythos 592.2%SourceDeepSeek V4 Pro 0813—Not directly comparable
Multimodal
Frequently asked questions
Which is better, Claude Mythos 5 or DeepSeek V4 Pro 0813?
Claude Mythos 5 has the higher public score, 82.87 versus 60.93, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.
Which is better for coding, Claude Mythos 5 or DeepSeek V4 Pro 0813?
Claude Mythos 5 leads the like-for-like coding comparison across 2 shared weighted benchmark rows.
Which is better for agentic tasks, Claude Mythos 5 or DeepSeek V4 Pro 0813?
The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.
Which costs less, Claude Mythos 5 or DeepSeek V4 Pro 0813?
For the stated presets, chat costs $0.035 on Claude Mythos 5 and $0.00087 on DeepSeek V4 Pro 0813; repository review costs $0.65 and $0.02436; the cache-heavy agent loop costs $0.9 and $0.01812. Costs use the listed standard API rates.
Which has the larger context window, Claude Mythos 5 or DeepSeek V4 Pro 0813?
Both models list the same context window, 1M.
Related comparisons
Last updated August 13, 2026
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