TokenMix Research Lab · 2026-04-07
Grok 4 Benchmark Comparison 2026: Grok 4.20 vs GPT-5.4 vs Claude Opus 4.6 -- SWE-bench, MMLU, and Cost Per Benchmark Point
Last Updated: 2026-04-29
Author: TokenMix Research Lab
Grok 4.20 hits 78% SWE-bench (3.5 points behind GPT-5.4 at 81.5%, 2 points ahead of Claude Opus 4.6); Grok 4.1 Fast at 70% SWE-bench is the budget tier leader. Both ship with 2M token context — largest in industry.
Grok 4 is xAI's strongest model family to date, and the benchmark numbers back it up. Grok 4.20 hits 78% on SWE-bench Verified, placing it within striking distance of GPT-5.4 and ahead of Claude Opus 4.6 on several coding benchmarks. Grok 4.1 Fast trades 8 points on SWE-bench for an 90% price cut and keeps the same 2M context window. This article breaks down every major benchmark score for both Grok 4 variants, compares them head-to-head against GPT-5.4, Claude Opus 4.6, and DeepSeek V4, and calculates which model gives you the most benchmark performance per dollar spent. All benchmark data compiled from official sources and verified by TokenMix.ai as of April 2026.
Table of Contents
- Quick Grok 4 Benchmark Overview
- Why Grok 4 Benchmark Scores Matter in 2026
- Grok 4.20 Benchmark Deep Dive
- Grok 4.1 Fast Benchmark Analysis
- Grok 4 vs GPT-5.4: Head-to-Head Benchmark Comparison
- Grok 4 vs Claude Opus 4.6: Where Each Model Wins
- Grok 4 vs DeepSeek V4: Budget Tier Benchmark Battle
- Full Benchmark Comparison Table
- Cost Per Benchmark Point: The Value Analysis
- How to Choose: Grok 4 Decision Guide
- Conclusion
- FAQ
Quick Grok 4 Benchmark Overview
Grok 4.20 ranks 3rd on SWE-bench (78%) behind GPT-5.4 (81.5%) but output is 60% cheaper at $6/M vs $15/M. Grok 4.1 Fast (70% SWE-bench) beats DeepSeek V4 on coding benchmarks at the same price tier.
All scores represent the latest publicly available results, cross-referenced with TokenMix.ai tracking data as of April 2026.
| Benchmark | Grok 4.20 | Grok 4.1 Fast | GPT-5.4 | Claude Opus 4.6 | DeepSeek V4 |
|---|---|---|---|---|---|
| SWE-bench Verified | 78.0% | 70.0% | 81.5% | 76.0% | 68.5% |
| MMLU | 91.2% | 87.5% | 92.0% | 90.8% | 89.5% |
| GPQA Diamond | 74.5% | 68.0% | 76.2% | 73.8% | 70.1% |
| HumanEval | 94.0% | 90.5% | 95.2% | 93.5% | 91.0% |
| MATH-500 | 96.5% | 93.0% | 97.1% | 95.8% | 94.2% |
| Coding Arena (Elo) | 1385 | 1310 | 1410 | 1370 | 1295 |
| Context Window | 2M | 2M | 1.1M | 1M | 1M |
| Input/M tokens | $2.00 | $0.20 | $2.50 | $3.00 | $0.30 |
| Output/M tokens | $6.00 | $0.50 | $15.00 | $15.00 | $0.50 |
Key takeaway: Grok 4.20 ranks third on SWE-bench behind GPT-5.4, but its output pricing is 60% cheaper. Grok 4.1 Fast beats DeepSeek V4 on every benchmark while matching its price tier.
Why Grok 4 Benchmark Scores Matter in 2026
Five companies now score above 70% on SWE-bench — three years ago that number was zero. The right question isn't "which model wins benchmarks" but "which model wins per dollar at my workload." The AI model landscape in April 2026 is the most competitive it has ever been. Five companies have models scoring above 70% on SWE-bench Verified. Three years ago, that number was zero.
What makes Grok 4 benchmarks worth a dedicated analysis is the pricing context. xAI has consistently positioned Grok as the performance-per-dollar leader. That claim needs verification with actual numbers.
Benchmarks alone do not tell the full story. SWE-bench measures real-world software engineering capability. MMLU tests broad knowledge. GPQA Diamond targets PhD-level reasoning. HumanEval and coding arena scores test raw programming ability. Each benchmark captures a different dimension of model capability.
The question developers are actually asking is not "which model has the highest score" but "which model gives me the best results for my specific workload at a price I can afford." That requires looking at benchmark scores and pricing together -- which is exactly what TokenMix.ai tracks across 300+ models.
Grok 4.20 Benchmark Deep Dive
Grok 4.20 scores 78% SWE-bench, 91.2% MMLU, 1385 coding arena Elo, 2M context — third behind GPT-5.4 on accuracy but ahead on context (2× larger) and output cost (60% cheaper). Grok 4.20 is xAI's flagship model, released in March 2026. It operates in both reasoning and non-reasoning modes, with both sharing the same benchmark ceiling.
SWE-bench Verified: 78.0%
Grok 4.20 scores 78% on SWE-bench Verified, making it the third-highest performing model on this benchmark behind GPT-5.4 (81.5%) and ahead of Claude Opus 4.6 (76.0%). The 3.5-point gap to GPT-5.4 is meaningful but not decisive -- it translates to roughly 7 fewer resolved issues out of 200 test cases.
Where Grok 4.20 shows particular strength is in multi-file refactoring tasks. The 2M context window allows it to hold entire repositories in context, reducing the need for retrieval-augmented approaches that other models depend on for large codebases.
MMLU: 91.2%
At this tier, MMLU scores are compressed. The difference between 91.2% (Grok 4.20) and 92.0% (GPT-5.4) is less than 1 percentage point. All five frontier models score above 89% on MMLU, making it a poor differentiator at the top end.
Coding Arena: 1385 Elo
The Chatbot Arena coding leaderboard places Grok 4.20 at 1385 Elo, behind GPT-5.4 (1410) and slightly ahead of Claude Opus 4.6 (1370). Arena scores reflect human preference in head-to-head comparisons, which captures code quality, explanation clarity, and debugging ability -- dimensions that SWE-bench does not fully measure.
GPQA Diamond: 74.5%
PhD-level science reasoning shows Grok 4.20 performing competitively at 74.5%. This is a 1.7-point gap behind GPT-5.4 and within 1 point of Claude Opus 4.6. For scientific computing and research applications, all three models are viable.
What it does well:
- Multi-file code changes leveraging 2M context
- Reasoning-heavy tasks when reasoning mode is enabled
- Long-document analysis where context length is the bottleneck
Trade-offs:
- 3.5% behind GPT-5.4 on SWE-bench
- Non-reasoning mode sacrifices chain-of-thought for speed
- Smaller training data ecosystem compared to OpenAI and Anthropic
Best for: Teams that need near-frontier performance with 2x the context window at 60% lower output cost than GPT-5.4.
Grok 4.1 Fast Benchmark Analysis
Grok 4.1 Fast at $0.20/$0.50 hits 70% SWE-bench — highest score in the budget tier — with a 2M context window that no other budget model offers. DeepSeek V4 manages 68.5% at slightly higher input price. Grok 4.1 Fast is the budget variant in the Grok 4 family. At $0.20/$0.50 per million tokens, it competes directly with DeepSeek V4 ($0.30/$0.50) and GPT-5.4 Nano ($0.20/$1.25).
SWE-bench Verified: 70.0%
The 70% SWE-bench score is the standout number. At its price point, no other model comes close. DeepSeek V4 manages 68.5%, and GPT-5.4 Nano does not publish an official SWE-bench score. Getting 70% SWE-bench performance at $0.20 input pricing was unthinkable 12 months ago.
MMLU: 87.5%
A 3.7-point drop from Grok 4.20 on MMLU. Adequate for most production applications but noticeable on specialized knowledge tasks.
Coding Arena: 1310 Elo
The 75-point Elo gap between Grok 4.1 Fast and Grok 4.20 is significant. In practical terms, Grok 4.20 would win roughly 60% of head-to-head coding comparisons. But at 10x the price, those extra wins come at a steep premium.
Context Window: 2M (Same as Grok 4.20)
This is the underrated advantage. Grok 4.1 Fast retains the full 2M context window of its flagship sibling. DeepSeek V4 offers 1M. GPT-5.4 Nano offers 400K. For agent workflows that need to process large codebases or document sets at budget pricing, this 2M window is a category-defining feature.
What it does well:
- Budget-tier pricing with mid-tier benchmark scores
- 2M context window at $0.20/M input -- unmatched
- Reasoning mode toggle for flexible deployment
Trade-offs:
- 8 points behind Grok 4.20 on SWE-bench
- Weaker on complex multi-step reasoning
- Lower coding arena Elo suggests less polished code output
Best for: High-volume production workloads, agent frameworks, and any application where context length matters more than peak accuracy.
Grok 4 vs GPT-5.4: Head-to-Head Benchmark Comparison
GPT-5.4 wins every benchmark by 1-4 points but Grok 4.20 saves 60% on output ($6/M vs $15/M) and ships nearly 2× the context (2M vs 1.1M). For most production work, the benchmark gap is within noise; the cost gap is not. GPT-5.4 is the current benchmark leader across most categories. The question is whether its lead justifies its pricing premium.
| Benchmark | Grok 4.20 | GPT-5.4 | Gap | Grok 4.20 Cost Advantage |
|---|---|---|---|---|
| SWE-bench | 78.0% | 81.5% | -3.5% | 60% cheaper output |
| MMLU | 91.2% | 92.0% | -0.8% | 60% cheaper output |
| GPQA Diamond | 74.5% | 76.2% | -1.7% | 60% cheaper output |
| HumanEval | 94.0% | 95.2% | -1.2% | 60% cheaper output |
| Coding Arena | 1385 | 1410 | -25 Elo | 60% cheaper output |
| Context | 2M | 1.1M | +900K | Grok wins |
The pattern is consistent: GPT-5.4 leads every benchmark category by 1-4%, but Grok 4.20 output costs $6.00/M versus $15.00/M. That is a 60% savings on every generated token.
For most production workloads, the benchmark gap is within noise range. A 3.5% difference on SWE-bench means GPT-5.4 resolves 7 more issues out of 200. Whether that matters depends entirely on your use case.
Where GPT-5.4 genuinely pulls ahead is on the most complex multi-step reasoning chains. If your application involves 10+ step planning or highly adversarial code generation, GPT-5.4's edge compounds. For standard coding assistance, summarization, analysis, and content generation, Grok 4.20 delivers comparable quality at a fraction of the cost.
The context window gap is harder to ignore. Grok 4.20's 2M context is nearly double GPT-5.4's 1.1M. For repository-scale code analysis or processing long documents, this difference changes what is architecturally possible without chunking.
Grok 4 vs Claude Opus 4.6: Where Each Model Wins
Grok 4.20 beats Claude Opus 4.6 on every benchmark dimension AND costs 33% less on input, 60% less on output. Opus retains an edge only on instruction precision and Anthropic's safety alignment for enterprise. Claude Opus 4.6 is Anthropic's flagship and the preferred model for many enterprise deployments. Here is how it stacks up against Grok 4.
| Benchmark | Grok 4.20 | Claude Opus 4.6 | Winner |
|---|---|---|---|
| SWE-bench | 78.0% | 76.0% | Grok 4.20 (+2.0%) |
| MMLU | 91.2% | 90.8% | Grok 4.20 (+0.4%) |
| GPQA Diamond | 74.5% | 73.8% | Grok 4.20 (+0.7%) |
| HumanEval | 94.0% | 93.5% | Grok 4.20 (+0.5%) |
| Coding Arena | 1385 | 1370 | Grok 4.20 (+15 Elo) |
| Context | 2M | 1M | Grok 4.20 (2x) |
| Input/M | $2.00 | $3.00 | Grok 4.20 (33% cheaper) |
| Output/M | $6.00 | $15.00 | Grok 4.20 (60% cheaper) |
Grok 4.20 leads Claude Opus 4.6 on every benchmark dimension and costs significantly less. The SWE-bench gap is 2 full percentage points. The output price gap is 60%.
Where Claude Opus 4.6 retains an advantage is in instruction following precision, safety alignment, and long-form structured output. Enterprise teams that need deterministic formatting, tool-use reliability, and Anthropic's safety guarantees may still prefer Claude despite the benchmark and price disadvantage.
For raw coding and reasoning performance per dollar, Grok 4.20 is the better value proposition against Claude Opus 4.6 in April 2026.
Grok 4 vs DeepSeek V4: Budget Tier Benchmark Battle
Grok 4.1 Fast wins SWE-bench (+1.5%) and coding arena (+15 Elo); DeepSeek V4 wins MMLU (+2%) and GPQA (+2.1%). Grok wins on context (2M vs 1M) and input price (33% cheaper). Code-heavy → Grok; knowledge-heavy → DeepSeek. DeepSeek V4 is the default budget model for many developers. Grok 4.1 Fast challenges that position directly.
| Benchmark | Grok 4.1 Fast | DeepSeek V4 | Gap |
|---|---|---|---|
| SWE-bench | 70.0% | 68.5% | Grok +1.5% |
| MMLU | 87.5% | 89.5% | DeepSeek +2.0% |
| GPQA Diamond | 68.0% | 70.1% | DeepSeek +2.1% |
| HumanEval | 90.5% | 91.0% | DeepSeek +0.5% |
| Coding Arena | 1310 | 1295 | Grok +15 Elo |
| Context | 2M | 1M | Grok (2x) |
| Input/M | $0.20 | $0.30 | Grok (33% cheaper) |
| Output/M | $0.50 | $0.50 | Tied |
The budget tier comparison is more nuanced. DeepSeek V4 edges ahead on MMLU and GPQA by 2 points. Grok 4.1 Fast wins on SWE-bench and coding arena. The real differentiator is not benchmarks -- it is the 2M context window and 33% cheaper input pricing.
For code-heavy workloads, Grok 4.1 Fast has the edge. For knowledge-intensive tasks, DeepSeek V4 is slightly stronger. For anything requiring more than 1M tokens of context, Grok is the only option at this price.
Full Benchmark Comparison Table
Eight-model matrix shows GPT-5.4 leading SWE-bench at 81.5%, Grok 4.20 second at 78%, Claude Opus 4.6 third at 76% — all top frontier models cluster within 6 points. Both Grok variants ship with 2M context, no competitor matches.
Complete cross-model benchmark comparison, all scores verified by TokenMix.ai, April 2026.
| Benchmark | Grok 4.20 | Grok 4.1 Fast | GPT-5.4 | GPT-5.4 Mini | Claude Opus 4.6 | Claude Sonnet 4.6 | DeepSeek V4 | DeepSeek R1 |
|---|---|---|---|---|---|---|---|---|
| SWE-bench | 78.0% | 70.0% | 81.5% | 65.0% | 76.0% | 72.5% | 68.5% | 66.0% |
| MMLU | 91.2% | 87.5% | 92.0% | 85.0% | 90.8% | 88.5% | 89.5% | 87.0% |
| GPQA Diamond | 74.5% | 68.0% | 76.2% | 62.0% | 73.8% | 69.0% | 70.1% | 71.5% |
| HumanEval | 94.0% | 90.5% | 95.2% | 87.0% | 93.5% | 91.0% | 91.0% | 88.5% |
| MATH-500 | 96.5% | 93.0% | 97.1% | 90.0% | 95.8% | 93.5% | 94.2% | 95.0% |
| Coding Arena | 1385 | 1310 | 1410 | 1240 | 1370 | 1320 | 1295 | 1270 |
| Context | 2M | 2M | 1.1M | 400K | 1M | 1M | 1M | 128K |
| Input/M | $2.00 | $0.20 | $2.50 | $0.20 | $3.00 | $3.00 | $0.30 | $0.55 |
| Output/M | $6.00 | $0.50 | $15.00 | $1.25 | $15.00 | $15.00 | $0.30 | $2.19 |
Cost Per Benchmark Point: The Value Analysis
Grok 4.1 Fast and DeepSeek V4 tie at $0.007 per SWE-bench point (best value); Grok 4.20 at $0.077 is 2.4× more efficient than Claude Opus ($0.197) or GPT-5.4 ($0.184). At 500M tokens/month Grok 4.20 saves $4,584 vs GPT-5.4.
Raw benchmark scores tell you how good a model is. Cost per benchmark point tells you how efficient your spending is. This is the metric that matters for production budgets.
TokenMix.ai calculates cost efficiency as: output price per million tokens divided by SWE-bench score. Lower is better.
SWE-bench Cost Efficiency (Output $/M per % point)
| Model | SWE-bench | Output/M | Cost per SWE-bench % Point |
|---|---|---|---|
| Grok 4.1 Fast | 70.0% | $0.50 | $0.007 |
| DeepSeek V4 | 68.5% | $0.50 | $0.007 |
| Grok 4.20 | 78.0% | $6.00 | $0.077 |
| GPT-5.4 Mini | 65.0% | $1.25 | $0.019 |
| DeepSeek R1 | 66.0% | $2.19 | $0.033 |
| Claude Opus 4.6 | 76.0% | $15.00 | $0.197 |
| GPT-5.4 | 81.5% | $15.00 | $0.184 |
Grok 4.1 Fast and DeepSeek V4 are tied for the best cost efficiency, both at $0.007 per SWE-bench percentage point. But Grok 4.1 Fast scores 1.5 points higher, making it the better absolute value.
Grok 4.20 at $0.077 per point is 2.4x more cost-efficient than Claude Opus 4.6 ($0.197) and 2.4x more efficient than GPT-5.4 ($0.184). You get roughly the same tier of performance for less than half the per-point cost.
Monthly Cost Projections at Different Usage Levels
Assuming a 1:3 input-to-output ratio (typical for code generation workloads):
Low volume (1M output tokens/month):
| Model | Monthly Cost | SWE-bench Score |
|---|---|---|
| Grok 4.1 Fast | $0.57 | 70.0% |
| DeepSeek V4 | $0.60 | 68.5% |
| Grok 4.20 | $6.67 | 78.0% |
| GPT-5.4 | $15.83 | 81.5% |
| Claude Opus 4.6 | $16.00 | 76.0% |
Medium volume (50M output tokens/month):
| Model | Monthly Cost | SWE-bench Score |
|---|---|---|
| Grok 4.1 Fast | $28.33 | 70.0% |
| DeepSeek V4 | $30.00 | 68.5% |
| Grok 4.20 | $333.33 | 78.0% |
| GPT-5.4 | $791.67 | 81.5% |
| Claude Opus 4.6 | $800.00 | 76.0% |
High volume (500M output tokens/month):
| Model | Monthly Cost | SWE-bench Score |
|---|---|---|
| Grok 4.1 Fast | $283.33 | 70.0% |
| DeepSeek V4 | $300.00 | 68.5% |
| Grok 4.20 | $3,333.33 | 78.0% |
| GPT-5.4 | $7,916.67 | 81.5% |
| Claude Opus 4.6 | $8,000.00 | 76.0% |
At high volume, the gap between Grok 4.20 ($3,333/month) and GPT-5.4 ($7,917/month) is $4,584/month -- over $55,000/year in savings. That buys a lot of 3.5% SWE-bench gap.
Which Grok 4 Model Should You Choose?
Default to Grok 4.1 Fast (70% SWE-bench at $0.20/$0.50 with 2M context); escalate to Grok 4.20 for the 20% of complex tasks where the 8-point gap matters; pick GPT-5.4 only if the last 3.5% accuracy is non-negotiable.
| Your Situation | Recommended Model | Why |
|---|---|---|
| Need absolute best coding performance, budget is secondary | GPT-5.4 | Leads SWE-bench by 3.5% over Grok 4.20 |
| Need near-frontier performance, want to save 60% on output | Grok 4.20 | 78% SWE-bench at $6/M output vs $15/M |
| Need 2M context for repository-scale analysis | Grok 4.20 or 4.1 Fast | Only models with 2M context at any price |
| Budget-constrained production workloads | Grok 4.1 Fast | 70% SWE-bench at $0.50/M output |
| Enterprise with strict safety/compliance requirements | Claude Opus 4.6 | Anthropic's safety guarantees, despite benchmark gap |
| Knowledge-heavy tasks on a budget | DeepSeek V4 | 2 points ahead of Grok 4.1 Fast on MMLU/GPQA |
| Maximum throughput, low latency required | Grok 4.1 Fast | Budget pricing + reasoning mode toggle |
| Multi-model strategy for cost optimization | Mix via TokenMix.ai | Route complex tasks to Grok 4.20, simple to 4.1 Fast |
Related: See how all models rank on our LLM leaderboard and benchmark guide
What's the Verdict on Grok 4 Benchmarks?
Grok 4.20 is the most cost-efficient frontier model in 2026 (78% SWE-bench at 60% under GPT-5.4 output cost). Grok 4.1 Fast is the unmatched budget pick (70% SWE-bench + 2M context at $0.20/$0.50). 2M context is the underrated category-defining advantage. Grok 4 benchmark scores confirm xAI's position as a serious contender in the frontier model race. Grok 4.20 at 78% SWE-bench is not the overall benchmark leader -- GPT-5.4 holds that title at 81.5%. But the 60% output cost advantage makes Grok 4.20 the most cost-efficient frontier model available in April 2026.
Grok 4.1 Fast is the more interesting story for production teams. At $0.20/$0.50 with 70% SWE-bench and a 2M context window, it occupies a unique niche that no other model fills. DeepSeek V4 is close on benchmarks but has half the context. GPT-5.4 Nano is close on input price but costs 2.5x more on output with one-fifth the context window.
The 2M context window across both Grok 4 variants is the underappreciated advantage. When your workflow involves processing entire codebases, long document sets, or multi-turn agent conversations, context length stops being a spec sheet number and starts being the architectural constraint that determines what is possible.
For teams managing multi-model deployments, TokenMix.ai provides unified API access to both Grok models alongside GPT-5.4, Claude, and DeepSeek -- with real-time benchmark and pricing tracking to optimize routing decisions. Check tokenmix.ai for current data.
FAQ
Is Grok 4.20 better than GPT-5.4 for coding?
No. GPT-5.4 leads Grok 4.20 on SWE-bench by 3.5 percentage points (81.5% vs 78.0%) and by 25 Elo points on the coding arena leaderboard. However, Grok 4.20 output costs 60% less ($6/M vs $15/M), making it the better value for teams where that performance gap is acceptable.
What is Grok 4.1 Fast's SWE-bench score?
Grok 4.1 Fast scores 70.0% on SWE-bench Verified. At its price point ($0.20 input / $0.50 output per million tokens), this is the highest SWE-bench score available in the budget model tier as of April 2026.
How does the Grok 4 2M context window compare to competitors?
Grok 4's 2M token context window is the largest among major API providers. GPT-5.4 offers 1.1M, Claude Opus 4.6 offers 1M, and DeepSeek V4 offers 1M. Both Grok 4.20 and Grok 4.1 Fast share the same 2M window, making the budget variant particularly compelling for context-heavy workflows.
Should I use Grok 4.20 or Grok 4.1 Fast?
Use Grok 4.20 when you need frontier-level accuracy -- complex multi-step reasoning, difficult code generation, or tasks where an 8% SWE-bench gap matters. Use Grok 4.1 Fast for everything else: production workloads, agent frameworks, high-volume processing, and any task where 70% SWE-bench accuracy is sufficient. The 10x cost difference means most teams should default to 4.1 Fast and escalate to 4.20 selectively.
How does Grok 4 benchmark performance compare to DeepSeek V4?
Grok 4.20 outperforms DeepSeek V4 by 9.5 points on SWE-bench (78.0% vs 68.5%) but costs significantly more. Grok 4.1 Fast beats DeepSeek V4 by 1.5 points on SWE-bench at 33% cheaper input pricing, with double the context window. For coding tasks, both Grok 4 models have the edge. DeepSeek V4 is slightly stronger on MMLU and GPQA knowledge benchmarks.
Where can I compare Grok 4 benchmark scores with other models in real time?
TokenMix.ai maintains a live benchmark and pricing tracker covering 300+ models including all Grok 4 variants. Scores are updated as new benchmark results are published, and pricing is monitored daily across all major API providers. Visit tokenmix.ai for the latest data.
Author: TokenMix Research Lab | Last Updated: April 2026 | Data Source: xAI Official Docs, TokenMix.ai, SWE-bench Leaderboard, Chatbot Arena