Agentic
Not comparable- DeepSeek V3
- 36.7
- Estimated · #130/152
- Granite 4.2 30B
- Not ranked
- Basis
- BenchAlign lane · 0 vs 3 public rows
- Reading
- Not comparable
DeepSeek V3 vs Granite 4.2 30B
Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.
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.
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
Granite 4.2 30B is not ranked on the public lane for coding, so no winner is named for coding.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
Granite 4.2 30B is not ranked on the public lane for agentic, so no winner is named for agentic.
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
The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. DeepSeek V3 does not fit this workload in one request. Granite 4.2 30B does not fit this workload in one request. Granite 4.2 30B has no comparable published API token rate.
Confidence: listed-rates
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
What is actually comparable
Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.
3 DeepSeek V3 only
3 Shared
11 Granite 4.2 30B only
- Shared results
- 3
- DeepSeek V3 only
- 3
- Granite 4.2 30B only
- 11
- Like-for-like categories
- 0 / 8
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
Not comparable- DeepSeek V3
- 39.3
- Estimated · #118/151
- Granite 4.2 30B
- Not ranked
- Basis
- BenchAlign lane · 2 vs 6 public rows
- Reading
- Not comparable
Reasoning
Not comparable- DeepSeek V3
- 41.2
- Unranked · 2 rankable rows
- Granite 4.2 30B
- 54.7
- Unranked · 2 rankable rows
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Knowledge
Not comparable- DeepSeek V3
- 39.3
- Estimated · #137/183
- Granite 4.2 30B
- Not ranked
- Basis
- BenchAlign lane · 2 vs 2 public rows
- Reading
- Not comparable
Math
Not comparable- DeepSeek V3
- 26.2
- Unranked · 1 rankable row
- Granite 4.2 30B
- Not ranked
- Basis
- Provisional lane · 1 vs 0 weighted rows
- Reading
- Not comparable
Multilingual
Not comparable- DeepSeek V3
- Not ranked
- Granite 4.2 30B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Multimodal
Not comparable- DeepSeek V3
- Not ranked
- Granite 4.2 30B
- Not ranked
- Basis
- Provisional lane · 0 vs 0 weighted rows
- Reading
- Not comparable
Instruction following
Not comparable- DeepSeek V3
- 39.9
- #104/123
- Granite 4.2 30B
- Not ranked
- Basis
- Provisional lane · 0 vs 1 weighted rows
- Reading
- Not comparable
| Category | DeepSeek V3 | Granite 4.2 30B | Basis | Reading |
|---|---|---|---|---|
| Agentic | 36.7Estimated · #130/152 | Not ranked | Not comparableBenchAlign lane · 0 vs 3 public rows | Not comparable |
| Coding | 39.3Estimated · #118/151 | Not ranked | Not comparableBenchAlign lane · 2 vs 6 public rows | Not comparable |
| Reasoning | 41.2Unranked · 2 rankable rows | 54.7Unranked · 2 rankable rows | Not comparableProvisional lane · 0 vs 0 weighted rows | Not comparable |
| Knowledge | 39.3Estimated · #137/183 | Not ranked | Not comparableBenchAlign lane · 2 vs 2 public rows | Not comparable |
| Math | 26.2Unranked · 1 rankable row | Not ranked | Not comparableProvisional lane · 1 vs 0 weighted rows | Not comparable |
| Multilingual | Not ranked | 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 |
| Instruction following | 39.9#104/123 | Not ranked | Not comparableProvisional lane · 0 vs 1 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.
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 Verified
Coding
GPQA
Knowledge
MMLU-Pro
Knowledge
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
- DeepSeek V3
- $0.00082
- Fits in one request
- Granite 4.2 30B
- Self-hosted; infrastructure cost varies
- Fits in one request
Granite 4.2 30B has no comparable published API token rate.
Repository review
50K fresh input + 3K output tokens
- DeepSeek V3
- $0.0168
- Fits in one request
- Granite 4.2 30B
- Self-hosted; infrastructure cost varies
- Fits in one request
Granite 4.2 30B has no comparable published API token rate.
Cache-heavy agent loop
200K cached + 20K fresh input + 10K output tokens
- DeepSeek V3
- $0.0304
- Does not fit in one request
- Granite 4.2 30B
- Self-hosted; infrastructure cost varies
- Does not fit in one request
- Cached-input rate unavailable
DeepSeek V3 does not fit this workload in one request. Granite 4.2 30B does not fit this workload in one request. Granite 4.2 30B 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.
SpecificationDeepSeek V3Granite 4.2 30B
Context window
Maximum documented context; output-token limits may be lower.
DeepSeek V3
128K
Granite 4.2 30B
API model ID
DeepSeek V3
Not sourced
Granite 4.2 30B
ibm-granite/granite-4.2-30b
IBM Granite 4.2 30B model cardCached-input rate
A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.
DeepSeek V3
$0.07 per 1M cached input tokens
Granite 4.2 30B
No comparable hosted API rate
IBM Granite 4.2 30B model cardDocumented inputs
DeepSeek V3
Not sourced
Granite 4.2 30B
Not sourced
Documented outputs
DeepSeek V3
Not sourced
Granite 4.2 30B
Not sourced
Provider availability
DeepSeek V3
Not sourced
Granite 4.2 30B
Not sourced
Reasoning profile
DeepSeek V3
Non-Reasoning
Granite 4.2 30B
Reasoning
Weight access
DeepSeek V3
Open Weight
Granite 4.2 30B
Open Weight
License
DeepSeek V3
Open Weight
Granite 4.2 30B
Open Weight
Release date
DeepSeek V3
2024-12-26
Granite 4.2 30B
2026-08-25
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
- Both models list 128K.
Run the same representative tasks against both endpoints before changing production traffic.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
DeepSeek V3
API / mo$1,028
Self-host / mo$18,221
Break-even1.2B/day
Granite 4.2 30B
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Benchmark evidence
The full public result ledger is available for audit without forcing a wide desktop table onto a phone.
Browse raw public benchmark evidence17 rows
Agentic
Coding
LiveCodeBench
DeepSeek V337.6%SourceGranite 4.2 30B—Not directly comparable
SWE-bench Verified
DeepSeek V342%SourceGranite 4.2 30B57%SourceGranite 4.2 30B leads this result
SWE Multilingual
DeepSeek V3—Granite 4.2 30B41.9%SourceNot directly comparable
SWE-bench Pro
DeepSeek V3—Granite 4.2 30B33.3%SourceNot directly comparable
Terminal-Bench 2.1
DeepSeek V3—Granite 4.2 30B29.2%SourceNot directly comparable
LiveCodeBench v6
DeepSeek V3—Granite 4.2 30B75.8%SourceNot directly comparable
SciCode
DeepSeek V3—Granite 4.2 30B38.8%SourceNot directly comparable
Knowledge
Math
Instruction following
Frequently asked questions
Which is better, DeepSeek V3 or Granite 4.2 30B?
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, DeepSeek V3 or Granite 4.2 30B?
Granite 4.2 30B is not ranked on the public lane for coding, so no winner is named for coding.
Which is better for agentic tasks, DeepSeek V3 or Granite 4.2 30B?
Granite 4.2 30B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.
Which costs less, DeepSeek V3 or Granite 4.2 30B?
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, DeepSeek V3 or Granite 4.2 30B?
Both models list the same context window, 128K.
Related comparisons
Last updated September 10, 2026
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