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Model comparison

Qwen3.7 Plus vs Sarvam 105B

Data verified

Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

67.22/100
Margin
24.3pts
← winning
42.97/100
0 category wins0 category wins

Public leaderboard positions: Qwen3.7 Plus #21 (Supported); Sarvam 105B #157 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. Qwen3.7 Plus and Sarvam 105B share 11 comparable benchmark results. 0 of 8 categories are comparable. 58 results are unique to Qwen3.7 Plus; 0 to Sarvam 105B.

Updated July 23, 2026
Shared results
11
Qwen3.7 Plus only
58
Sarvam 105B only
0
Comparable categories
0 / 8

Benchmark data for Qwen3.7 Plus and Sarvam 105B is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.

Why this result

BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.

Qwen3.7 Plus has the larger context window at 1M, compared with 128K for Sarvam 105B.

Category breakdown

Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.

Category scores and score margins for Qwen3.7 Plus and Sarvam 105B
CategoryQwen3.7 PlusΔSarvam 105B
AgenticQwen3.7 Plus71.7MarginNo overlapSarvam 105BNot measured
CodingQwen3.7 Plus75.6MarginNo overlapSarvam 105BNot measured
ReasoningQwen3.7 Plus91.7MarginNo overlapSarvam 105BNot measured
KnowledgeQwen3.7 Plus60.1MarginNo overlapSarvam 105BNot measured
MathQwen3.7 Plus92.9MarginNo overlapSarvam 105BNot measured
MultilingualQwen3.7 Plus85.4MarginNo overlapSarvam 105BNot measured
MultimodalQwen3.7 Plus81.5MarginNo overlapSarvam 105BNot measured
Inst. FollowingQwen3.7 Plus84.5MarginNo overlapSarvam 105BNot measured

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricQwen3.7 PlusSarvam 105BComparison
Input / output priceUSD per 1M tokensQwen3.7 PlusNot availableSarvam 105B$0 input / $0 outputA complete price comparison is not available.
Generation speedtokens per secondQwen3.7 PlusNot availableSarvam 105BNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenQwen3.7 PlusNot availableSarvam 105BNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensQwen3.7 Plus1MSarvam 105B128KQwen3.7 Plus lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkQwen3.7 PlusSarvam 105BResult
Terminal-Bench 2.0Source 70.3%Not comparable
QwenClawBenchSource 61.8%Not comparable
QwenWebBenchSource 1536Not comparable
Claw-EvalSource 62.7%Not comparable
BFCL v4Source 72.9%Not comparable
MCP AtlasSource 73.2%Not comparable
VITA-BenchSource 45.6%Not comparable
DeepPlanningSource 62.3%Not comparable
OSWorld-VerifiedSource 73.3%Not comparable
AndroidWorldSource 81.0%Not comparable
AA Agentic IndexSource 20.8%Not comparable
APEX-Agents-AASource 22.4%Not comparable
τ²-bench resultsSource 93%46.8%Qwen3.7 Plus leads
GDPval-AASource 21.8%Not comparable
GDPval-AASource 936Not comparable
OSWorld 2.0Source 2.8%Not comparable
Coding
BenchmarkQwen3.7 PlusSarvam 105BResult
Terminal-Bench 2.0Source 70.3%Not comparable
SWE-bench VerifiedSource 77.7%Not comparable
SWE-bench ProSource 57.6%Not comparable
SWE MultilingualSource 75.8%Not comparable
NL2RepoSource 41.1%Not comparable
SciCodeSource 51.3%Not comparable
LiveCodeBenchSource 89.6%Not comparable
AA Coding IndexSource 55.9%Not comparable
AA-SciCodeSource 45.5%26.4%Qwen3.7 Plus leads
Reasoning
BenchmarkQwen3.7 PlusSarvam 105BResult
CritPtSource 9.1%0.0%Qwen3.7 Plus leads
MRCRv2Source 91.7%Not comparable
AA-LCRSource 65.0%0.0%Qwen3.7 Plus leads
Knowledge
BenchmarkQwen3.7 PlusSarvam 105BResult
GPQASource 90.3%Not comparable
GPQA-DSource 90.3%Not comparable
HLESource 34.7%Not comparable
MMLU-ProSource 88.5%Not comparable
MMLU-ReduxSource 94.5%Not comparable
SuperGPQASource 71.4%Not comparable
MMMLUSource 89.0%Not comparable
Artificial Analysis Intelligence IndexSource 39.0%11.9%Qwen3.7 Plus leads
AA-GPQA DiamondSource 90.0%73.8%Qwen3.7 Plus leads
AA-HLESource 33.4%10.1%Qwen3.7 Plus leads
AA-Omniscience IndexSource 2.4%-59.5%Qwen3.7 Plus leads
AA-Omniscience AccuracySource 22.2%17.6%Qwen3.7 Plus leads
AA-Omniscience Hallucination RateSource 25.5%93.5%Qwen3.7 Plus leads
Math
BenchmarkQwen3.7 PlusSarvam 105BResult
HMMT Feb 2026Source 92.9%Not comparable
IMOAnswerBenchSource 86.0%Not comparable
ApexSource 22.7%Not comparable
Multilingual
BenchmarkQwen3.7 PlusSarvam 105BResult
MMLU-ProXSource 85.4%Not comparable
NOVA-63Source 58.8%Not comparable
INCLUDESource 83.0%Not comparable
MAXIFESource 88.8%Not comparable
PolyMathSource 84.0%Not comparable
Multimodal
BenchmarkQwen3.7 PlusSarvam 105BResult
MMMU-ProSource 79%Not comparable
MathVisionSource 90.3%Not comparable
CharXivSource 85.9%Not comparable
ERQASource 69.8%Not comparable
MedXpertQA (MM)Source 71.0%Not comparable
ScreenSpot ProSource 79.0%Not comparable
SimpleVQASource 81.7%Not comparable
MMSearch-PlusSource 41.4%Not comparable
RealWorldQASource 86.9%Not comparable
OmniDocBench 1.5Source 91.4%Not comparable
OCRBench V2Source 70.7%Not comparable
ODINW13Source 51.1%Not comparable
Video-MME (with subtitle)Source 88.0%Not comparable
VideoMMMUSource 85.4%Not comparable
MLVU (M-Avg)Source 87.4%Not comparable
AA-MMMU-ProSource 80.5%Not comparable
Design Arena WebsiteSource 1288Not comparable
Inst. Following
BenchmarkQwen3.7 PlusSarvam 105BResult
IFEvalSource 94.6%Not comparable
IFBenchSource 79.1%Not comparable
AA-IFBenchSource 78.0%34.4%Qwen3.7 Plus leads
Frequently Asked Questions (3)

Can I compare Qwen3.7 Plus and Sarvam 105B on BenchLM yet?

Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.

Why does this comparison show “coming soon”?

BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.

What data is available for Qwen3.7 Plus and Sarvam 105B today?

Sarvam 105B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.

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Last updated: July 23, 2026

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